Background: The effect of hearing impairment on speech perception was described by Plomp (1978) as a sum of a loss of class A, due to signal attenuation, and a loss of class D, due to signal distortion. While a loss of class A can be compensated by linear amplification, a loss of class D, which severely limits the benefit of hearing aids in noisy listening conditions, cannot. The hearing loss of class D is assumed to be the main reason why not few users of hearing aids keep complaining about the limited benefit of their devices in noisy environments. Working compensation strategies against it are unknown.Methods: Recently, in an approach to model human speech recognition by means of a re-purposed automatic speech recognition (ASR) system, the loss of class D was explained by introducing a level uncertainty which reduces the individual accuracy of spectro-temporal signal levels. Based on this finding, an implementation of a patented dynamic range manipulation scheme (PLATT) is proposed which aims to mitigate the effect of increased level uncertainty on speech recognition in noise by expanding spectral modulation patterns in the range of 2 to 4 ERB. This compensation approach is objectively evaluated regarding the benefit in speech recognition thresholds in noise using the ASR-based speech recognition model. Recommendations for an evaluation with human listeners are derived.Results: The objective evaluation suggests that approximately half of the class D loss due to an increased level uncertainty might be compensable. To measure the effect with human listeners, an experiment needs to be carefully designed to prevent the confusion class A and D loss compensations.Conclusions: A working compensation strategy for the class D loss could provide previously unexploited potential for relief. Evidence has to be provided in experiments with human listeners. To this day, hearing aids without directional amplification or directional noise suppression provide their users only with limited benefit in noisy listening conditions. The limited benefit of hearing aids in noisy listening conditions is long known and was extensively described and put into context byThe class A loss, according toRecently,To induce a class A loss in the model, variations in the internal spectro-temporal signal levels below the individual hearing threshold, determined by the individual audiogram, were removed. This manipulation is illustrated in the center panel oflevel uncertainty. This manipulation is illustrated in the lower panel inTo induce a class D loss in the model, random values were drawn from a normal distribution and added to the internal spectro-temportal signal levels, where the standard deviation of the normal distribution was a variable calledcan be compensated by amplification as one would expect from a class A loss. The right panel shows FADE simulations with increasing values for the level uncertainty, which do not converge for high noise levels, that is, the manipulationscannot be compensated by amplification as one would expect from a class D loss. An important observation ofA)Present an approach which is able to partially compensate a class D loss as implemented with the level uncertainty in FADE, andB)objectively evaluate this approach and come up with testable quantitative hypothesis on its benefit in noisy listening conditions.If a mechanism that removes the information in the auditory system of listeners with impaired hearing works similar to the level uncertainty, than the level uncertainty can be seen as the functional counter-part to a compensation strategy for a class D hearing loss. This is exactly what will be assumed in the remainder of this contribution to what can be considered theoretical audiology. In the context of FADE, this will allow to generate testable hypothesis on the achievable benefit in speech recognition performance due to a possible compensation strategy for a class D loss. The aim of this contribution is to:1)need to be protected?Which portions, or more precisely, patterns of the speech signal carry the most relevant information and2)can be protected, given the strict constrains on the available future temporal context of the signal (approximately 1 ms) in hearing aid applications?Which signal patterns3)How can such a protection be achieved?For an effective mitigation of the effect of the level uncertainty on speech recognition performance, three main problems need to be addressed:the standard features used for ASR, MFCCsContinuing to follow an (assumed) analogy of basic principles in human and machine speech recognition, literature on robust automatic speech recognition provides hints on which signal patterns may be relevant for good (automatic) speech recognition performance in noise. Let us assume that a log Mel-spectrogram, such as it used for the calculation of the widely used Mel-frequency cepstral coefficient (MFCC) features, is representative for the information that is available to an ASR system. The spectral resolution of such a log Mel-spectrogram, of which an example is depicted in the upper panel ofams c.f.. By omitThe restriction to the availability of approximately 1 ms future temporal context in hearing aids does not allow for the reliable manipulation of temporal modulations below approximately 500 Hz. This limit is still way above the temporal modulation frequencies that are represented in the features of ASR systems. With the common analysis window length and shift of 25 and 10 ms, respectively, as used inIn combination of the answers to problems 1) and 2): spectral modulations just below 0.25 cycles per ERB, let’s say spectral patterns between 2 and 4 ERB, seem to be a good first candidate for protecting them against the level uncertainty. How can these signal patterns be protected or at least hardened against the effect of the internal noise due to the level uncertainty? In the logarithmic level domain (in the log Mel-spectrogram) the noise of the level uncertainty is additive, and one effective way of mitigating the effect of an additive noise is amplification. This means, that the desired spectral modulation patterns should be amplified, that is, expanded before the noise of the level uncertainty can remove that information.At a first glance, an expansion of signal dynamic in the context of the reduced residual dynamic range of listeners with impaired hearing might seem completely undesirable: the expansion of spectral modulations can result in uncomfortably high and/or inaudibly soft signal levels; which can occur at the same time in different frequency ranges. However, considering the scale (2 to 4 ERB) it is clear, that this is a region which is not modified by common approaches to multi-band dynamic compression, where the signal is usually independently compressed in approximately six bands. The traditional approaches to multi-band dynamic compression with less than 9 bands leave spectral patterns with a width of 2 to 4 ERB virtually uncompressed, which might also be interpreted as an indication towards the importance of these patterns. If now the expansion of one  part of the spectral dynamic increases the total spectral dynamic range of the signal, it might be possible to counter it with a stronger compression of other (less relevant) parts of the spectral dynamic. However, compression should only be applied if it is required, that is, if the input signal dynamic range does not fit into the available output dynamic range. The dynamic range of speech in noise is less than the dynamic range of a clean speech signal. Especially when considering speech in only slightly fluctuating noises at signal-to-noise ratios (SNRs) around 0 dB, the dynamic range of the mixture can be close to the minimum which is required to discriminate words. In this condition, no improvement can be expected from compressing the signal dynamic. Hence, a micro-management of the limited residual dynamic range to optimize its use for speech-recognition-relevant information is a desirable goal. PLATT is an approach which gives preference to spectral modulation patterns that are supposedly relevant for speech recognition at the expense of spectral modulation patterns which are supposedly less relevant when mapping an input signal dynamic to a given output signal dynamic. The here outlined ideas are part of a German patent (DE 10 2017 216 972 B4); a patent application in Europe is pending.Methods, were already engineered towards low-delay real-time processing. In a simpler implementation, the representation of the log Mel-spectrogram could be manipulated in the MFCC domain, where the spectral modulations are suitably encoded for this operation, and which in MFCC-terminology would be calledliftering. The inverse transform of the modified MFCCs, with amplified spectral modulation patterns between 0.25 and 0.125 cycles per band, could be compared to the unmodified log Mel-spectrogram, and a suitable filter response in the time-domain could be designed for each signal frame. While such an approach would have the advantage of simplicity The implementation details of PLATT, which are presented in the1)and  empirically.Which listening conditions, that is, which speech test and maskers, are suitable to evaluate the PLATT implementation objectively with FADE2)Which listener profiles are suited to clearly demonstrate a  compensation of a class D loss like it is implemented in FADE.For an evaluation of the implementation with respect to a possible compensation of a class D loss, the following points need to be considered:The first point is important to enable the verification with empirical data of any hypotheses that are based on the model predictions.N1 (corresponding to a very mild hearing loss) do not converge with the data of the normal-hearing profileNone up to noise levels of 90 dB SPL. Hence, even for very small increases in hearing threshold, amplification improves the SRT in the fluctuating noise condition up to very high presentation levels. With the aim of clearly attributing compensation strategies to compensate a class A or a class D loss, this is highly undesirable. To clearly identify the compensation of a class D loss, simple linear amplification alone must not improve the SRT. The reason for the observed model behavior is that, for the Bisgaard profiles, the frequency range above the hearing threshold increases with the presentation level. While the limited frequency range can safely be assumed a factor contributing to a class D loss and has to be considered in a suitable listener profile, it must be avoided that the effectively used frequency range changes at high presentation levels. One option would be to low-pass filter the speech material. Another option is to define profiles with very steep sloping hearing loss functions. The former option would be very suitable to measure empirical data. The latter option is regarded cleaner from a modeling perspective, because it reduces the number of parameters that influence the SRT and results in a simpler and possibly better traceable model. Hence, listener profiles with normal hearing thresholds below, and infinite hearing loss above a given limit frequency are suitable for the considerations in this contribution. While the measurements fromThe second point, the selection of suitable listener profiles, is a bit more complex than it might initially appear. A sensible approach would be to take the individual profiles inferred from the psychoacoustic measurements by•The applicable functional description of a process that is assumed to cause a class D hearing loss, the level uncertainty, can be used to design a matched functional compensation strategy.•Simulation experiments with FADE can be conducted which quantify the compensation of a class D loss as implemented with the level uncertainty as a function of frequency range and level uncertainty.•These experiments can later also be conducted with human listeners to test the hypothesis.With these considerations the following steps are enabled:The methods described in the following were used to simulate speech recognition experiments in stationary and fluctuating noise at different presentation levels for 16 listening profiles with class D hearing losses without and with the later proposed dynamic range expansion by PLATT including different degrees of expansion.The speech material of the  German matrix sentence test  was usedSpeech recognition tests were simulated with an ASR-based approach and their outcome, the SRT-50, was predicted based on the simulation results. The simulations were performed with the latest standard version of FADEListener profiles and the processing of the noisy speech signals with PLATT as explained in the sectionPLATT dynamic range manipulation. Hence, the FADE simulation method is only outlined here, and we refer the interested reader to the original description fromThe speech tests considered in the sectionPredictions with FADE are performed completely independently for each listening condition . There is no dependency on any empirically measured SRT, nor on predictions of the same model in other/reference conditions (and hence no need to define such). This means, with FADE, a single SRT of a speech recognition test for which no empirical data exists can be predicted. For the prediction of one outcome the following standard procedure as described byListener profiles. Subsequently, an ASR system using whole-word models implemented with Gaussian Mixture Models and Hidden Markov Models, was trained on the features. This resulted in 50 whole-word models for each training SNR. These models were then used with a language model that considers only valid matrix sentences (of which 105 exist) to recognize test sentences on a broad range of SNRs with noisy speech material from the same considered condition. For each combination of a training SNR and a test SNR, the transcriptions of the test sentences were evaluated in terms of the percentage of correctly recognized words. The resulting recognition result map  speech signals, features were extracted, where this step included the implementation of the class D hearing loss, as described in the sectionIntroduction, two types of manipulations which induce class D hearing loss were considered: 1) a limitation of the frequency range, 2) an increase of level uncertainty. The effect of both parameters on the log Mel-spectrogram of a clean speech sample is depicted inOutcome predictions of speech recognition tests as well as basic psychoacoustic tests with FADE were found to be close to the empirical results for listeners with normal hearing . As propIn this section, the patented (DE 10 2017 216 972 B4) PLATT dynamic range manipulation as it was conceived for a later implementation in a hearing device is described. The implementation was optimized to run in real-time on a Raspberry Pi 3 Model B to enable field studies with mobile hearing aid prototype hardwareIntroduction to the PLATT concept Conditions in which the available dynamic range for acoustic communication is reduced are rather the norm than the exception. For example, in a driving car, the lower limit of the available dynamic range is determined by the driving noise. Or in a library, the upper limit is given by the accepted sound levels in such an environment. And, importantly, the available dynamic range for communication is limited for listeners with impaired hearing. For a successful communication, it may be required to adapt a source signal, which may contain speech and non-speech parts, to the available dynamic range on the receiver side by dynamic range compression. But, in many real-time applications, the available temporal context to perform this operation is very limited.spectral modulation, is preserved. Static dynamic range compression only preserves the spectral modulation within each independent frequency band, but not across bands, even if the dynamic range would be available. Also, fewer frequency channels reduce the need for sharp filters which would require long integration time constants and introduce additional latency.Multi-band dynamic range compressors used in hearing (aid) research e.g. statical“Both studies showed that compression of the lowest order component  had by far the largest effect, and that compression of higher order components had little effect, if any.” The common idea behind these two studies was to linearly map and manipulate the spectral dimension of a suitable spectro-temporal representation with the aim of separating important from less important speech signal dynamic. However, both studies considered only clean speech signals, and hence did not consider the relevant portions of speech signal for their recognition in noise.the technical solution to decode speech signals and hence provide a model for speech recognition. As outlined in theIntroduction, the feature extraction stages of ASR systems provide representations of the speech dynamic that are well suited for the robust recognition of speech in noise. However, the often employed basis for the feature extraction stages, the log Mel-spectrogram, is not suited for low-latency signal processing due to its long integration window. The relatively long integration window of the log Mel-spectrogram serves two objectives: 1) obtain a sufficiently high frequency resolution to separate low-frequency signal content into approximately 1 ERB-wide bands, and 2) ensure that in voiced speech portions each signal frame contains at least one pulse . Fortunately, these two aspects  are compatible and can be optimized for low-latency processing at the cost of a frequency-dependent group delay. In the following, the design of PLATT, a fast adaptive dynamic range manipulation scheme that takes the mentioned observations into account, is proposed, where the following three objectives were pursued:•Preservation and enhancement of spectral modulations which are assumed to be relevant for speech recognition•Low-latency and fast reaction time while minimizing audible artifacts•Adaptive limitation of the compression to the necessary minimumThat the signal dynamic can be described as the difference of frequency-dependent short-term effective amplitudes, e.g., across time , across frequency , or both , raises the question which representation is most suitable to manipulate it. ASR systems are1)An auditory-motivated frequency decomposition and re-synthesis of the audio signal, which allows to manipulate frequency- and time-dependent amplitudes in a perceptually relevant domain and helps to ensure that only limited audible artifacts can be introduced.2)Extraction of a spectro-temporal representation from the frequency-decomposed signal which is similar to those used for robust ASR and hence suitable for the analysis of the relevant spectral modulations.3)Adaptive calculation of frequency- and time-dependent gains from the spectro-temporal representation which uses compression only as required to provide high speech recognition performance when the available dynamic range on the output side is limited.PLATT consists of three functional parts:Data availability).Frequency decomposition & re-synthesis The frequency decomposition of the input signal is performed with a filter bank of fourth-order Gammatone filters. To get a set of frequencies which are relevant for (automatic) speech recognition, the center frequencies are chosen equidistantly on a Mel-frequency scale with half the distance that is commonly used for calculating MFCCs. Considering frequencies in the range from 64Hz to 16000Hz results in the following 78 +4 values:  123, 155, 187, 221, 256, 293, 330, 370, 410, 453, 496, 542, 589, 638, 689, 742, 797, 854, 914, 975, 1039, 1105, 1174, 1245, 1319, 1396, 1476, 1559, 1645, 1734, 1827, 1923, 2023, 2127, 2235, 2346, 2462, 2583, 2708, 2838, 2972, 3112, 3257, 3408, 3565, 3727, 3896, 4071, 4253, 4441, 4637, 4840, 5051, 5270, 5498, 5734, 5979, 6233, 6497, 6771, 7056, 7352, 7658, 7977, 8307, 8650, 9006, 9376, 9760, 10158, 10572, 11001, 11447, 11909, 12390, 12888, 13406, 13943,  Hz . Only the 78 filters with center frequencies from 123 Hz to 13943 Hz, spaced approximately 0.5 equivalent rectangular bandwidths (ERB), are used. The -10 dB-bandwidth of each fourth-order Gammatone filter is chosen to be equal to the difference of the frequencies two positions right and left to its center frequency, e.g., 221 Hz−93 Hz = 128 Hz for 155 Hz. The aim is to evenly cover the relevant frequency range with filters that have a bandwidth similar to auditory filters (≈ 1 ERB) and allow a trivial re-synthesis in the time domain by simple summation of all filter bank outputs. With this goal, the filter coefficients are determined as follows: The pole in the complex z-plane that describes the frequency-dependent properties of a first-order infinite impulse response Gammatone filter is calculated according to the formulac the center frequency of the corresponding fourth-order filter, and fb the sampling frequency. The phase of the single FIR coefficient of each filter is chosen such that the phases of each pair of fourth-order filters with neighboring center frequencies were identical at the delay where the product of their respective temporal envelopes reaches its maximum. This happens to be the case at different delays for different pairs and minimizes destructive interference between the corresponding filter outputs. In addition, the absolute value of the FIR coefficient of each filter is chosen such that the maximum gain is 2. Together, a) evenly covering frequencies, b) avoiding destructive interference, and c) normalizing the maximum gain result in a very flat frequency response for the sum of all filter bank channels.Spectro-temporal signal representation A representation of the spectral dynamic which encodes information similar to a log Mel-spectrogram is determined as follows, based on the real-valued output of the filter bank. For each filter output, the values are held for 15 ms and decay subsequently with a rate ofBecause a pure tone is not changed in amplitude by a filter of the employed Gammatone filter bank when it matches the filters center frequency, the calibration of its input and output values is identical. Assuming a calibrated setup, only values above the frequency-dependent normal hearing threshold, as defined by the International Organization for Standardization in its standard 226:2003 , are conAdaptive spectral gain The adaptive determination of time- and frequency-dependent gains takes into account the current spectral input dynamic and the currently available output dynamic. It aims to minimize the compression with the constraint to avoid masking the signal parts which could carry important (speech) information. It also allows to expand the spectral modulations that are assumed to be important for speech recognition and trade the such increased signal dynamic against an increased compression of less relevant signal dynamic.base layer. The base layer contains a very coarse description of the spectral dynamic and resolves spectral patterns larger than approximately 16 ERB. The other layers, the differences 4 to 1, resolve the spectral dynamic which is not described in the base layer with decreasing pattern sizes.The spectral input dynamic is analyzed with spectral modulation low-pass filters. For this, each vector of the 78 spectral values is convolved with Hanning windows of the following widths to obtain increasingly spectrally smoothed versions of the initial vector: 8, 16, 32, and 64, which approximately correspond to a full width at half maximum (FWHM) of 2, 4, 8, and 16 ERB, respectively. The left panel ofThe base layer needs to be mapped from the input dynamic range to the output dynamic range. For the application in a hearing device, the mapping of the base layer could be performed with a prescription rule. In addition, the limits of the input and output dynamic ranges need to be defined. Here, for the input, a dynamic range that is probably relevant for normal hearing listeners is assumed: A frequency-independent uncomfortable level of 105dB SPL as the upper limit, and levels of 25dB SPL from 500Hz to 4kHz, and 30dB above 8kHz and below 250Hz, as the lower limit. The assumed input dynamic range is indicated with triangles in the left panel inIn our example inunconditionally. The example with two pure tones is an extreme one which assesses the maximum dynamic that can be encoded in each difference, which is about 6 dB for difference 1. The corresponding output dynamic, when only adding difference 1 to the base layer, can be observed in the center panel ofTo prefer spectral patterns which are important for robust (automatic) speech recognition, the differences corresponding to high spectral modulation frequencies are added first. The highest spectral modulation frequencies are encoded in difference 1 and account only for a very small part of the total dynamic, which is why it is addedconditionally and possibly partially according to the following constraints: 1) For each frequency, the respective difference is multiplied with the highest possible factor between 0 and 1 for which its addition to the current output dynamic  does not result in output values above the upper limit or below the lower limit. For example, if the current value is 80dB SPL, the difference +30dB SPL, and the limit 90dB SPL, only a third of difference can be added and the factor isThe remaining differences 3, and subsequently 4, are addedAdmittedly, in our example with the two pure tones, there is only energy at 500 Hz and 2000 Hz, and hence only the level of the two tones will be changed, while the gains at other frequencies will have no effect. However, this signal creates a pattern of extreme spectral modulation in the proposed signal analysis, and hence is well suited to illustrate how PLATT works. For a signal with only low spectral modulations, no conditional compression would be required.Summary of PLATT dynamic range manipulation With PLATT, compression is only applied if the available output dynamic range is less than required to represent the input signal dynamic. The main effect can be observed inhigh (solid curves) andreduced (dotted curves) spectral input dynamic are shown for an exemplarily reduced output dynamic range, as indicated by the triangles. While the signals with high spectral dynamic are compressed (observed here as different gains for different frequencies) to make relevant signal portions audible and avoid excessively high output levels, the signal with low spectral dynamic is not (observed here as similar gains for different frequencies), because it fits in the available output dynamic range without compression. Only low spectral modulation frequencies, which are less important for speech recognition, are conditionally compressed, while the important higher spectral modulation frequencies can be even expanded. An efficient implementation of spectral modulation filtering in the time-domain is possible with reasonably low latency, fast reaction times, and probably little audible artifacts due to the auditory-motivated signal decomposition and re-synthesis.Compensation of level uncertainty with PLATT The possibility to selectively expand the spectral modulation patterns between 2 and 4 ERB with PLATT was used to protect these patterns against the effect of the level uncertainty as implemented in FADE. With the aim to best decouple the evaluation of the expansion from the conditional compression feature of PLATT, the available output dynamic range was set to match the input dynamic range, and the base layer mapping was set to identity. This minimizes compression and effectively disables the fine-grained compression management for all but very low and very high signal levels. Nonetheless, in view of expanding a part of the signal dynamic, such a management will probably be needed in an application with a limited output dynamic range to counter the expansion with a possible compression of other parts of the signal dynamic.after the processing. This justifies the expectation that the expansion might protect important speech patterns against level uncertainty. With the fluctuating masker, the speech patterns are not as easily distinguishable from the background noise. But comparing the representation of the unprocessed (center) and processed (bottom) signals, more of the patterns that are hidden by the level uncertainty in the unprocessed signal are discernible in the processed variant. This illustrates that the expansion does not selectively process the speech portions of the signal but generally protects the spectral modulation patterns between 2 and 4 ERB against the level uncertainty, independently of what caused these patterns. Also observable, the processing with PLATT-6 results in a possible increase in the output level. This affirms the need to evaluate the method in a context where simple time-invariant linear amplification will not change the speech recognition performance and thus decouple the effect of the expansion from the effect of any linear amplification.Expansion factors of 2, 4, 6, and 8 were considered for the evaluation, and the corresponding compensation conditions are referred to as PLATT-2 to PLATT-8 in the remainder of the manuscript. The effect of processing noisy speech signals at 0 dB SNR with PLATT-6 is illustrated inThe just described effects of an expansion of the spectral modulation patterns between 2 and 4 ERB on noisy speech signals were quantified in simulation experiments with FADE. For this, SRTs with the German matrix sentence test were simulated for all combinations of considered noise maskers , noise presentation levels , listener profiles (P-8000-1 through P-1000-21), and compensations ; summing a total of 1760 outcome predictions with FADE.Key simulation outcomes are presented as “Plomp curves”, that is, SRT-50s in dB SPL as a function of the noise presentation level, analog toTwo modifications were used to implement a hearing loss of class D, the limitation of the frequency range up to a limit frequency, and the increase of the level uncertainty. Their separate effect on simulated SRTs is shown inThe limitation of the available frequency range affected the simulated SRTs in the fluctuating noise condition much more than in the stationary noise condition when assuming a level uncertainty of 1 dB cf.. In the The increase of the level uncertainty also affected the simulated SRTs in the fluctuating noise conditions more than in the stationary noise condition cf.. The effNeither implementation alone achieves to increase the SRT to above 0 dB with the considered parameter values. In listeners with impaired hearing, we assume that both factors, the level uncertainty as well as the limited frequency range, contribute to the class D loss. All combinations of both parameters were considered, of which a few are presented inIt cannot be expected that a partial expansion of the signal dynamic (e.g. with PLATT) will compensate the increase in SRT due to limiting the frequency range. However, the simulations suggest an interaction of the level uncertainty with the limited frequency range, more specifically, that an increase in level uncertainty would be worse in the stationary noise condition if the frequency range is limited. This is due to the strongly non-linear nature of the (automatic) speech recognition process, which can be interpreted to involve a forward error correction scheme that integrates over frequency and over time, and which needs to fail in order to achieve an error rate as high as 50%. Forward error correction requires redundancy, which is reduced by limiting the frequency range. The simulation result shows that considering level uncertainty and a limitation of the frequency range separately would not show the full picture.To graphically present effect of the PLATT compensation on the simulated SRTs, the mixed profiles P-4000-7, P-2000-14, and P-1000-21 were used. This choice reflects the assumption that the limitation of the frequency range (due to the audiogram) and an increase in level uncertainty are probably correlated to some extent. The simulation results with these listener profiles without PLATT compensation are shown inThe effect of the expansion with PLATT on the simulation results for listener profiles P-4000-7, P-2000-14, and P-1000-21 is presented inThe expansion with PLATT improved the SRTs in almost all simulated conditions. The benefit in SRT due to PLATT in was generally higher in the fluctuating noise condition than in the stationary noise condition. Higher expansion factors strongly tended to result in higher benefits, however, not in all conditions. As expected, the observed compensations were partial, that is, the simulated performance of the profile with normal hearing were not restored. For a quantitative analysis, the results are interpreted only at the levels at which an improvement in SRT due to simple linear amplification can be ruled out.The curves stabilize at high SRTs, that is, a further increase in levels does not improve the SRT anymore. In the stationary noise condition, this is the case for presentation levels of 40 dB SPL and above. But even above this level, the curves show some variability, which is due to the stochastic nature of the simulation process. The plotted data points were derived as the difference of two independent simulations. Assuming a random  prediction error of 0.5 dB, this would lead to a random error of the plotted data points of approximately ±0.71 dB, that is for 5 of 100 simulation results, on average, the error will be larger than ±1.96 ⋅ 0.71 ≈ ±1.39 dB. Hence, “stabilized” refers to reaching a state which may allow to assume that the remaining variability is due to the random error. In the fluctuating noise condition, the random error is increased, because of the generally shallower slope of the psychometric function in this condition. There, the curve of the normal-hearing profile stabilizes at levels of about 70 dB. The curves of the unaided and compensated simulations already stabilize at lower levels. Hence, at noise presentation levels of 70, 80, and 90 dB SPL an improvement in SRT due to simple linear amplification can be ruled out.For a break-down of the simulation results, the average improvements at noise presentation levels of 70, 80, and 90 dB SPL were calculated and are reported inThe tables present the simulated benefits in SRT using PLATT with expansion factors 2, 4, 6, and 8, for all 16 considered listener profiles, sorted by the limit frequency. In the last column, the benefit in SRT that corresponds to the normal-hearing (NH) profile is presented as an orientation to assess the proportion of the total class D loss that be compensated. Positive values indicate an improvement, that is, a lower SRT. An orientative value for the random error of the presented simulation data inFor listener profiles with a level uncertainty of 1 dB (P-*-1), only small benefits were observed. In the stationary noise condition, processing the signals with PLATT had no effect on the SRTs (≤0.4 dB). In the fluctuating noise condition, small positive and negative benefits (min. −2 dB and max. 2 dB) were observed; the SRTs for the listener profile P-1000-1 improved (lower SRTs) and the SRTs for the listener profiles P-8000-1 and P-4000-1 increased (negative improvement). This effect was most pronounced with PLATT-8 and PLATT-6, less pronounced with PLATT-4, and could not be observed with PLATT-2, that is, it depended on the expansion factor. The processing with PLATT, by increasing the amplitude of certain spectral modulations, decreases the prominence of (or “masks”) other modulation patterns, and it introduces a possible leak of information across frequencies, e.g., from >1000 Hz to <1000 Hz. While the masking of other modulation patterns could explain the detrimental effect of PLATT for the profiles P-8000-1 and P-4000-1, the leakage of information from higher frequencies could explain the improvement for profile P-1000-1. Despite these small variations of the SRTs due to the processing with PLATT in the fluctuating noise condition, one might feel comfortable to state: As expected, the effect of a limited frequency range (an increase in SRT that cannot be compensated by simple amplification) cannot be compensated with the expansion performed by PLATT.The data in the column NH, that is, the difference in SRT between the simulation with a specific listener profile and the normal-hearing profile, shows again how different the simulation results are in the stationary and fluctuating noise condition. While limiting the frequency range to 1000 Hz (P-1000-1) only resulted in a surprisingly small increase in SRT, namely by 2.3 dB, the same modification increased the SRT in the fluctuating noise condition by 15.0 dB. This reaffirms the use of different noise maskers to assess speech in noise recognition performance.Interpreting the addition of maskers to a speech signal as a frequency-dependent removal of information, the results indicate that the information in the mid-frequency range (1000-4000 Hz), was much more relevant in the considered fluctuating noise condition (ICRA5-250) than in the stationary noise condition (OLNOISE). In other words, in the stationary noise condition, this mid-frequency portion was mostly redundant at the SRT, and the ASR system could discriminate the 50 words of the matrix sentence test almost equally well only using the low-frequency information. This was not the case for the fluctuating noise condition, where the mid-frequency portions contributed a substantial part of the information to achieve low SRTs . For the following presentation of the benefits with PLATT, it is important to keep in mind that this missing information, by design, cannot be compensated with PLATT. That means, when considering the maximum achievable improvement for a specific profile, the increase in SRT due to limiting the frequency range was disregarded. For example, with profile P-2000-14 in the fluctuating noise condition, the difference to the normal-hearing SRT is 14.3 dB; improving the SRT by 14.3 dB the performance of the normal-hearing profile would be achieved. When only considering the increase in SRT due to limiting the frequency range, that is profile P-2000-1, the difference to the normal-hearing SRT is 6.5 dB. Hence, the maximum achievable improvement for profile P-2000-14 is then estimated by the difference 14.3−6.5 = 7.8 dB.For profiles with increased level uncertainty , the average benefits in SRT due to using PLATT were positive and ranged from 0.5 to 7.8 dB. For these profiles, the lowest improvements were found with PLATT-2, and the highest improvements often, but not always, with PLATT-8. Overall, in both noise conditions, a very strong relation between the increase in level uncertainty and the improvement in SRT due to using PLATT could be observed. Combined, there was strong general tendency that with higher level uncertainty and with higher expansion factors larger improvements were observed. Exceptions were observed only for the profiles with a level uncertainty of 7 dB (P-*-7), where higher expansion factors did not further increase the improvement. This data supports the sensible expectation that lower expansion factors are sufficient  to compensate lower values of level uncertainty. For profiles with a level uncertainty of 14 or 21 dB, the improvements always increased together with the expansion factor. With high values of level uncertainty, higher expansion factors might further improve the SRT; however, increasing the dynamic of a signal portion eight fold might have undesirable collateral effects which are discussed later.In absolute terms, the improvements were generally larger in the fluctuating noise condition than in the stationary noise condition. For example with the extreme profile P-1000-21 and PLATT-8 compensation, the improvement was 5.3 dB in the stationary noise condition, and 7.8 dB in the fluctuating one. However, relating the improvement to the performance with the normal-hearing profile  generally prefer to have conversations at higher SNRs than the SRT-50, that is, at SNRs at which more than 50% of the words can be correctly recognized; which might correspond to something like the SRT-80. The SRT-80 can be simulated, but it cannot be as efficiently and accurately measured in listening experiments as the SRT-50, because of the shallower slope of the psychometric function at the SRT-80. The main reason for simulating the SRT-50 was that it can be accurately measured in later listening experiments with human listeners, and accurate measurements are a requirement to show effects as small as 1 dB. To assess if the improvements would (at least according to the model) translate to improvements at SRTs preferred in real conversations, the psychometric functions of aided and unaided conditions were compared. Segments of psychometric functions were obtained by evaluating simulations at SRT-20, SRT-25, ..., SRT-90.Introduction, an important assumption was that the part of the hearing loss which cannot be explained by the absolute hearing threshold, that is, the missing piece to describe the effect of hearing loss on speech recognition in noise, can be explained by the level uncertainty. Another assumption was, that this model parameter also affects tone in noise perception and hence its value could be inferred from tone in noise detection tests. While the results supported this hypothesis, evidence was not sufficient to rule out other mechanisms that would also increase the SRTs in noise. This is a fundamental problem in modeling the individual speech recognition performance. While the quantity that is predicted by the model, the SRT, can be measured in experiments with human listeners, the outcome of such measurements still depends on many correlated and non-linearly interacting parameters; some of which cannot be controlled very well, such as, e.g., attention. And even if the experimental results were measured with the most accurate methods, the measurement errors include this uncontrollable (human) variability which can increase the required amount of data to falsify hypotheses to infeasible regions. This is especially true for hypotheses which predict relatively small effects. Hence, there is reasonable doubt about whether the removal of information in listeners with impaired hearing is really well described by the level uncertainty, or if it just coincidentally increased the SRT in the correct conditions.The presented experimental results were derived using a model of auditory perception, more specifically, a model of impaired human speech recognition based on automatic speech recognition. The modeling approach with FADE brings assumptions about the impaired human speech recognition process into a form in which they can be tested by comparing predictions with empirical data. The employed model, the framework for auditory discrimination experiments (FADE) such as it was used byTo more specifically test if the level uncertainty is suitable to describe the effect of hearing loss on speech recognition in noise, a promising approach is to interact with it. The expansion of PLATT was specifically designed to interact with—namely compensate—the effect of the level uncertainty. The modeled data clearly showed this interaction. If this specific interaction was found in empirical data, it would be strongly supportive for the hypothesis of the existence of a mechanism in the human auditory system similar to the level uncertainty. Beyond the academical interest in the suitability of the assumptions to describe impaired human speech recognition performance, a positive result would have immediate practical implications for the design of hearing loss compensation strategies.A)Present an approach which is able to partially compensate a class D loss as implemented with the level uncertainty in FADE, andB)objectively evaluate this approach and come up with testable quantitative hypothesis on the benefit in noisy listening conditions.Let’s remember that the goal was not to test if the expansion with PLATT improves speech recognition performance of listeners with impaired hearing. For that, measurements with listeners with impaired hearing will be necessary. The aim of this contribution was to:The latter aim, in other words, was to guide the planning of the measurements with listeners with impaired hearing towards optimal evidence. Hence, the following discussion is oriented towards the planning of a suitable experiment.D). He reported average empirical values of SHLD from five investigations which range from as low as 1 dB to above 10 dB  the maximum average values were larger, up to 14 dB. The increase in SRT in noise was related to the increase in SRT in quiet SHLA+D in Figure 8 inA+D comes with a 1-dB increase of SHLD. This data indicated that, on average, about a third of the hearing loss in quiet (SHLA+D) was incompensable by simple amplification; that would bea lot. Listeners with speech hearing loss in quiet of 39 dB would have on average 13 dB of traditionally incompensible loss. Also,D. There is no reason to assume that SHLD is independent from the speech test. The main contribution to the random error (unpredictable differences across measurements) was probably different for the studies and due to the stochastic nature of the measurement procedures. Both, the random and the systematic errors were not specifically considered in the analysis ofD with the employed matrix sentence test. Matrix sentence tests were designed to minimize the random error in the measurement of an SRT, whereHowever, these findings have to be taken with care. The data used byD for the German matrix sentence test in the (stationary) test-specific noise condition, where the SRT in noise increased by about 1 dB every 10-dB increase in PTAA+D) includes the speech hearing loss in noise (SHLD), while the PTA might not. But that alone cannot explain the huge difference. Another reason for the difference could be thatFortunately, a suitable data set was measured with a matrix sentence test.D for PTAs of 60 dB HL could be about 6 dB in stationary noise. The individual variability of SHLD in their analysis was reported with 1.17 dB for PTAs below 47 dB HL, which was only twice the test-retest reliability of the matrix test. An interpretation of this relation could be that a given hearing loss in quiet is very likely related to a certain hearing loss in noise. This interpretation would be only valid if additional amplification would not change the relation, that is, if the same relation was observed for a noise presentation level of, e.g., 75 dB SPL instead of 65 dB SPL. However, it is difficult to speculate on that. On the one hand, the test-specific noise (OLNOISE) reduces the effect of the individual hearing threshold by masking the speech signals with a stationary matched-spectrum noise. On the other hand, for higher PTAs, the individual hearing threshold will eventually exceed the noise level at high frequencies, which could be compensated by amplification. Then again, according to the simulations presented in this contribution, the removal of high-frequency portions (>4000 Hz) of the signals in the OLNOISE condition had little effect on the SRT. These considerations demonstrate the inherently non-linear relations between the parameters assumed to affect speech recognition in noise, and hence the difficulty of abstracting these from a given context.Taking the relation fromD for the German matrix sentence test that could likely be found in the wild, the relation established byD of about 7 dB. This estimate can be used as an orientation to interpret the right-most column inD. For example, profile P-2000-14 with an SHLD of 6.5 dB lies within the range of empirically observed values. In this context, profiles P-1000-14 and P-2000-21 might be border cases with values of 9 to 10 dB for SHLD, and profile P-1000-21 lies probably outside range of empirically observed values for SHLD. Profiles P-8000-21, P-4000-14, P-2000-14, and P-1000-7, are all compatible with a large SHLD of less than 7 dB in the stationary noise condition. The key difference between these profiles is the proportion of SHLD that is due to the level uncertainty and hence might be compensable by a PLATT expansion. Assuming that the average available frequency range to listeners with impaired hearing is between 2000 and 4000 Hz, P-4000-14 and P-2000-14 could represent parameter values in a realistic range.Nonetheless, to continue with an specific value for SHLIntroduction, the most promising (and at the same time constructive) approach to test whether a mechanism similar to an increased level uncertainty causes a part of the class D hearing loss of listeners with impaired hearing is trying to compensate it.The aim of classifying profiles into more and less realistic ones is to use them to formulate a quantitative hypothesis on the expected benefit in SRT due to PLATT expansion. This does not mean that the considered profiles exist. The assumption that the reduction in speech recognition performance in noise is due to a combination of a limited frequency range and a mechanism similar to an increased level uncertainty justifies their use. While this assumption may be sensible, it doesn’t mean that it is correct. This would have to be tested in listening experiments. For the effect of the limited frequency range, this could be achieved by, e.g., low-pass filtering the signals in speech recognition experiments. For the effect of an increased level uncertainty, a direct verification is currently not possible because the level uncertainty adds a noise in a domain which is not accessible for manipulations in experiments with human listeners; unlike for the limitation of the frequency range, there is no known equivalent signal manipulation. As explained in theThe expansion feature of the PLATT dynamic range manipulation approach aims to mitigate the increase in SRT due to an increased level uncertainty. It was specifically designed to compensate the effect of the level uncertainty on speech recognition performance such as it is implemented in FADE. The presented simulation results showed that this necessary interim goal was achieved and indicate that about half of class D loss due to an increased level uncertainty was compensated. This only demonstrates that the expansion works as intended in the context of the model. There is no reason to assume that the results, that is, the partial compensation of a class D hearing loss, would transfer to experiments human listeners. Now—the key question of this contribution—what will happen in experiments with human listeners, in case the central model assumptions are incorrect?A)If the PLATT expansion does not improve the SRTs in noise of human listeners with a class D hearing loss, this would indicate that the mechanism that causes the class D loss is not well described by the level uncertainty, because the PLATT expansion interacts differently with the mechanism in human listeners than with the mechanism in the model.B)If the PLATT expansion improves the SRTs in noise of human listeners and if these improvements are in line with individual predictions, this would strongly support the hypothesis that a mechanism similar to an increased level uncertainty causes a part of the class D hearing loss of listeners with impaired hearing.C)However, more likely than B), if the PLATT expansion improves the SRTs in noise of human listeners but the improvements are found to be substantially smaller than predicted, this would only partly support the hypothesis that a mechanism similar to an increased level uncertainty causes a part of the class D hearing loss of listeners with impaired hearing.In scenario A), a central model assumption is incorrect. The (to-be) collected empirical data still can help to improve the model, but the expansion feature of PLATT would be useless in the context of a hearing device. In scenario B), the central model assumptions are probably correct. The (to-be) collected empirical data cannot help to improve the model, but the expansion feature of PLATT would overcome a serious limitation of current hearing device technology. In scenario C), most of the central model assumptions were probably correct. The (to-be) collected empirical data can probably help to improve the model, and the expansion feature of PLATT could give strong hints regarding how to overcome a serious limitation of current hearing device technology. In this last scenario, a differentiated analysis of the predictions errors will be helpful in tracking down the inaccurate assumptions. The probabilities for scenario A), B), and C) are unknown. To provide evidence for any scenario, an experiment with human listeners suitable to unmistakably show the compensation effect has to be conceived.The presented simulation results clearly show the potential of the expansion of spectral modulation patterns in the range between 2 and 4 ERB, as implemented in PLATT, to compensate a class D hearing loss that was implemented by an increased level uncertainty. In both noise conditions, approximately half of the class D loss due to an increased level uncertainty was compensated, while the class D loss due to a limited frequency range was not compensated. Narrowing down the simulation results to profiles P-4000-14 and P-2000-14, which were identified as the more realistic ones, the predicted improvements in SRT with PLATT-6 were 3.0 dB in the stationary noise condition. These improvements would correspond to a compensation ofThe optimal (in terms of improvements in SRT) value for the expansion factor depended on the level uncertainty parameter. For lower values of the level uncertainty, in many conditions lower factors (4 or 6) resulted in the best speech recognition performance, but higher values did not decrease the improvement much. For the profiles P-4000-14 and P-2000-14, a factor of 8 was optimal. From this perspective, nothing speaks against evaluating PLATT with high expansion factors in listening experiments. Because an expansion factor of 8 results in a strong modification of the signal, and because it is unknown how such modifications are perceived in terms of quality and loudness by listeners with impaired hearing, at least two values for the expansion factor should be evaluated in listening experiments.In measurements with human listeners, speech recognition performance, audio quality perception, and loudness perception all depend on the presentation level. In this contribution, efforts were made to separate the speech recognition performance from the presentation level as much as possible. This resulted in considering elevated presentation levels, and loudness perception is strongly related to presentation levels. Also in this contribution, efforts were made to mitigate audible artifacts in the manipulation and resynthesis stages of PLATT. But the non-linear manipulation inevitably results in audible artifacts which probably affect audio quality perception. The effect of processing noisy speech signals at 0 dB SNR with PLATT-1, PLATT-4, and PLATT-8 is illustrated inApart from speech recognition performance, individual loudness and audio quality perception are important perceptual dimensions for hearing aid users. The expected increase in perceived loudness and a possible decrease in perceived audio quality are considered collateral side-effects of a signal manipulation with PLATT. These side-effects can either be tolerated or mitigated/compensated with further extensions or modifications of the PLATT approach. It is unclear how a listener with impaired hearing would trade speech recognition performance for audio quality or loudness in the context of PLATT expansion. To gather evidence about the relation of PLATT expansion, loudness perception, and audio quality perception, the considered expansion factors should cover a wide range of these side-effects. Hence an evaluation of speech recognition performance, audio quality, and loudness with PLATT expansion factors of 1, 4, and 8 is proposed. This will help to better estimate the suitability of the raw approach in the context of hearing aids and show which properties could be in the focus for possible future optimization.There are several possibilities to optimize the expansion approach with respect to loudness perception and quality. For example, the expansion factor can be chosen to be frequency dependent. If the frequency regions in which the benefits are achieved by expansion do not fully overlap with the frequency regions where loudness perception is critical, there could be potential to favorably trade speech recognition performance against loudness. The expansion factor could be limited, or mapped otherwise non-linearly to only expand (the low) modulation amplitudes required to improve speech perception in low-SNR conditions while leaving  modulation amplitudes in high-SNR conditions unmodified. A detailed discussion of possible modifications is considered too hypothetical to be further elaborated in this contribution.Based on the presented simulations and considerations, the following experimental measurement conditions are proposed to test the presented hypothesis that a part of a class D hearing loss can be compensated by expanding spectral patterns in the range between 2 and 4 ERB in speech in noise recognition experiments with human listeners. Regarding the noise maskers, an evaluation with the test-specific noise (e.g. OLNOISE for the German matrix sentence test), the fluctuating ICRA5-250 noise, and, in addition, a competing voice masker, e.g., the International Speech Test Signal ISTS; or its oThe noise presentation levels ideally would be sufficiently high to compensate any hearing loss which is compensable by simple amplification. For the stationary noise condition, this would be easier to achieve than with the fluctuating noise conditions. As shown in left panel ofTo further mitigate any benefits due to simple linear amplification at high presentation levels, all stimuli should be low-pass filtered to achieve an effective frequency range of 2000 Hz. This would limit the evaluation of a benefit due to PLATT expansion to the frequency range up to 2000 Hz; the central frequency region that listeners with impaired hearing typically can use for speech recognition. A focus on this frequency region could be advantageous in a first evaluation. The low-pass filtering would make a group of listeners with impaired hearing less heterogeneous, because larger differences across listeners are usually observed at frequencies above 2000 Hz. And the loudness perception of such low-pass filtered stimuli might also be more pleasant than their corresponding broadband variants.In total, the proposed measurements comprise 18 conditions: Three for training, three unaided measurements at 70 dB SPL (one for each noise masker), three unaided measurements at 80 dB SPL , and nine measurements with PLATT-1, PLATT-4, and PLATT-8 . In all conditions, SRT, loudness perception and audio quality perception are recommended to be assessed.In addition, it is recommendable to characterize the listeners in the wayAs speech material for the SRT measurements, the optimized matrix test sentences in native language are recommendedFor a first approach, headphone measurements are preferable over free-field measurements because they can be performed monaurally, which is recommendable. A monaural presentation prevents the interference of possibly individual binaural effects and facilitates the comparison to model predictions, as discussed byOnce there is evidence whether the proposed compensation strategy has a positive effect on speech recognition performance of listeners with impaired hearing, the band-width limitation to 2000 Hz that was recommended for the first experiments should be removed. For the next steps, it would not be important anymore to demonstrate the compensation of a class D loss alone, but to show benefits in more realistic and individually optimized configurations. Hence, there should be a shift towards a joint individual optimization of the compensation of class A and class D loss, loudness perception, and audio quality. Then, care would need to be taken to individually normalize loudness perception to get meaningful data. If the expansion approach with PLATT proves to work in monaural listening conditions, the concept should be extended to a binaural listening condition. For this, a free-field setup with a mobile hearing aid prototype hardware is recommendable to correctly assess individual binaural hearing, including binaural loudness perception. To better understand which speech portions are most affected by PLATT expansion, it would also be interesting to study its effect on phonemic contrasts.•The functional modeling of the class D hearing loss with the framework for auditory discrimination experiments (FADE), implemented by means of the level uncertainty, was interpreted as the counterpart of a compensation strategy which aims to  compensate a class D heaing loss.•The strict low-delay constraints in hearing aid applications only allow for a manipulation of mainly spectral modulation patterns. Of these, the patterns in the range of 2 to 4 ERB seem especially suitable to protect them against the effect of the level uncertainty by dynamic range expansion.•A low-delay, real-time capable implementation of a patented dynamic range manipulation scheme (PLATT) which allows to perform the required dynamic range expansion was proposed. The implementation was optimized to run in real-time on the Raspberry Pi 3 Model B platform.•The evaluation of the PLATT expansion with FADE for several idealized profiles of hearing loss indicated that approximately half of the class D hearing loss due to an increased level uncertainty was compensable.•prior to performing these. The underling hypothesis, that a class D hearing loss can be  compensated, can be directly tested in an experiment with human listeners in the same listening conditions. Recommendations for this experiment were elaborated.Simulations with FADE were used to predict the outcomes of specific speech recognition experimentsThe most important findings of this work can be summarized as follows:Underlying datahttp://doi.org/10.5281/zenodo.4500810  is available from:http://medi.uni-oldenburg.de/download/ICRA/index.htmlThe ICRA5-250 noise signal is avaliable from:https://doi.org/10.5281/zenodo.4003779 (FADE version 2.4.0 was used for the FADE simulations:4003779 .http://htk.eng.cam.ac.uk/The Hidden Markov Toolkit (used by FADE) is available from: Main Commentstheoretical Audology”: This is a good paper. I agree with the author that it contributes to the scientific topic that may rightly be called “level uncertainty in the front-end processor of a simulation system for auditory discrimination (FADE), which uses an automatic speech recognition system to quantify the effects on speech recognition. The paper summarizes several earlier studies indicating that the specific difficulty of people with impaired hearing to understand speech in noise is caused by a reduced information-transmission capacity of the peripheral auditory system ("class D" loss). These earlier results also suggest that the information loss has similar effects as an artificially induced spectro-temporal Considering the hypothesis that the level uncertainty in the computational auditory model might correspond to some physiological processes in the auditory system of real people, the author has designed a signal-processing system (PLATT) that can be used in real hearing aids to compensate for the level uncertainty, by enhancing spectral modulation patterns that are most important to convey phonetic contrasts in real speech. The paper clearly explains the main features of the PLATT system, and presents simulation results showing that about half of the simulated loss caused  by the level uncertainty could be compensated by hearing-aid spectral expansion. Of course, the results from model simulations in a computer might be seen as just an advanced form of mathematical speculation, and the author wisely emphasises that these simulation results cannot prove that PLATT is beneficial for real listeners. Instead, the paper uses the simulation results as a starting point to discuss how an empirical speech recognition experiment should be designed to be able to test the two hypotheses, (1) that something like the modelled level uncertainty causes the speech-recognition loss in real listeners, and (2) that spectral modulation enhancement in a hearing aid can (at least partly) compensate for this loss. The paper shows that it is far from trivial to design such an experiment with real listeners. The experiment should separate the specific effects of modulation expansion from the effects of linear (or slow-compression) amplification, because both types of processing can have effects on speech recognition, loudness, and subjective quality of the processed speech in noise. Another serious difficulty is the test-retest uncertainty of all speech-recognition test methods. I think all of this discussion is scientifically valuable and should be approved for indexing. I have only a few suggestions to make the presentation easier to read and understand. Suggestions for Clarity:Temporal modulation patterns: The second point in the Conclusions states that hearing aids “… only allow for a manipulation of mainly spectral modulation patterns.” This formulation seems a little too strong: All compression systems have some effects on the temporal “modulation patterns” of the input signal. For example, slow automatic gain control (AGC) (with long attack time and long release time) tends to compress only the very slow temporal modulations while leaving faster modulations nearly unchanged. People have also studied AGC systems controlled by several signal features in parallel, with different time constants, with more complex effects on the temporal modulation patterns. The proposed PLATT system also “manipulates” the temporal “modulation patterns”, although it was not specifically designed for that purpose.Figure Captions and Table Header: I guess I am not the only reader tempted to look at the figures before reading the detailed explanations in the surrounding text. I understand the figure caption can not include a long complicated explanation, but, to facilitate understanding, the figure captions should at least define all graphical elements seen in the figure. No problem if the caption gets long. Examples:Figure 9: This figure is complicated but very important for readers to understand, as it is central for the explanation of PLATT. The caption should clearly state that the labels 1,2,3,4 refer to thedifferences between the displayed curves, because text labels in a graph most often refer to the plotted curves, not to thedistance between curves. Perhaps this could be even more clearly shown graphically by a vertical line indicating the difference, connected to the arrow line to the label.not the difference between the correspondingly light-gray curves in the OUTPUT and the INPUT plots, which would be the default interpretation. Instead, a gray GAIN curve is the difference between the gray OUTPUT  spectrum and the solid black curve for the total INPUT spectrum. This should be explained in the caption. I spent some time searching the text before I found this explanation. Also, at first sight, I was confused by the use of grayscale: The lightest gray curve in the GAIN plot isFigure 10: The caption text seems to suggest that there are three spectra with only pure tones, and another three spectra with added white noise, but there is only one dotted curve, showing the spectrum of the pure tones with peaks about 60 dB SPL with added white noise. Figures 12-18: The second and fourth plot titles state “difference in SRT”, but the plot actually shows a positive “difference in SRT”, i.e., an increased SRT as a negative number, because it indicates a decrease in performance. This is of course easy to understand, but should still be mentioned in the first caption. Also, the meaning of legend text strings, like “P-8000-1 none” must be explained in the caption. At first sight of Fig 12, I could not guess the intended meaning of “none”.Table 1: The column with heading “NH” should be explained more clearly in the table header. The numerical values in this column do actuallynot indicate “the normal-hearing listener profile” as stated. The numbers show thedifference between results for the NH and the unaided impaired profile on the same row in the first column. I needed to read the explanation on the following page before I understood that the numbers in the “NH” column are meant to be interpreted as ideal measures to which the benefit measures shown in the “PLATT-x” columns might be compared.Is the work clearly and accurately presented and does it cite the current literature?YesIf applicable, is the statistical analysis and its interpretation appropriate?YesAre all the source data underlying the results available to ensure full reproducibility?YesIs the study design appropriate and is the work technically sound?YesAre the conclusions drawn adequately supported by the results?YesAre sufficient details of methods and analysis provided to allow replication by others?YesReviewer Expertise:Probabilistic pattern recognition, model-based signal processing, hearing technology, psycho-acoustics.I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. The paper considers hearing impairment as a sum of loss due to attenuation and loss due to distortion, as proposed by Plomp (1978). It considers, in particular, the contribution of the distortion component  and asks how much the deficit caused by this component might be restored by well-designed dynamic range manipulation. A patented dynamic range manipulation scheme (PLATT) is presented which works by decomposing spectral modulations into different frequency bands and applying a compression scheme that treats each band separately. The benefit of PLATT for ameliorating distortion loss is then tested by measuring the predicted SRT-50 improvements for speech in different noise conditions and listeners with different hearing profiles . These experiments have used conditions that isolate the effect of distortion loss .  The FADE (ASR-based) model is used to predict listener behaviour. The author is clear to point out that the purpose of the paper is not to make specific claims for the benefit of PLATT in real listening conditions, but instead, to make specific and testable predictions about the outcomes of (yet-to-be-performed) listening experiments. In particular, the experiments will test the hypothesis that the distortion component of hearing loss can be modelled by level-uncertainty. The paper concludes by discussing the design of the listening studies and by presenting thoughts on the future development and evaluation of PLATT in real-world listening conditions where both distortion and attenuation need to be considered. I greatly enjoyed reading this well-written, detailed and thought-provoking paper. I have no major criticisms but have listed a few points for clarification below and a number of specific typos that can be fixed.For clarificationOn page 19, the paper says "the curves show some variability which is due to the stochastic nature of the simulation process." I was not quite sure how to interpret this. Is this stochasticity in the FADE model, e.g. in the ASR training and testing? If so could the variability in the result be reduced by averaging over a greater number of simulated runs. If not would it be useful to add error bars to the Plomp plots.On page 26, the hypothesis and outcomes of the future listening experiments are very clearly stated, and it is anticipated that the PLATT expansion may improve SRTs by less than predicted. It is said that this will be taken to mean that increased level uncertainty causes only a part of the class D hearing loss. However, could there be other reasons why listeners would find it hard to process the PLATT signals. For example, they might successfully reduce uncertainty but produce signals that do not match their expectations of speech signals, i.e., in FADE the ASR system is trained extensively on the processed signal, but in the listening experiments, listeners have their own internal speech models. Would listeners need extensive listening to adapt to the signals?On page 29 it is noted that FADE is a poor predictor of SRTs for speech in the presence of speech interferers. I think it would be interesting to say a little about why this might be the case. Is it that it does not explicitly model informational masking effects, or that it does not model source separation? And if so are these factors that could be at play in the processing of the speech plus ICRAS-250 noise? And could these factors interact with the PLATT processing? e.g., cues for source separation being compromised by the non-linear PLATT processing.  Specific points and typographical errors, Page 3,"This limit is still way above" -> "This limit is still far above"  Page 8, end of first paragraph, "be performed human listeners" -> "be performed by human listeners" Page 10, "fewer frequency channels reduce" -> "using fewer frequency channels reduces" Page 11, "raises the question which representation" -> "raises the question of which representation" Page 11, Figure 5, please remake with larger text Page 12, after eq (1), "and f_b the sampling frequency" -> "and f_s the sampling frequency" (or change f_s to f_b in the equation) Page 13, Spectro-temporal signal representation: the envelope estimate would be clearer if presented with an equation.  Page 16, after 1760 at end of page add, (2 x 11 x 16 x 5) just to be clear about how the total number of conditions arises. Page 18, line 2, "as one might have expect." -> "as one might have expected." Page 18, start of final paragraph, "present effect of" -> "present the effect of" Page 19, paragraph 2, "due to PLATT in was" -> "due to PLATT was" Page 25, final paragraph, "with an specific" -> "with a specific"Is the work clearly and accurately presented and does it cite the current literature?YesIf applicable, is the statistical analysis and its interpretation appropriate?Not applicableAre all the source data underlying the results available to ensure full reproducibility?YesIs the study design appropriate and is the work technically sound?YesAre the conclusions drawn adequately supported by the results?YesAre sufficient details of methods and analysis provided to allow replication by others?YesReviewer Expertise:Automatic speech recognition, machine listeningI confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard.


The results of recent animal studies have suggested that cochlear synaptopathy may be an important factor involved in presbycusis. Therefore, here, we aimed to examine whether cochlear synaptopathy frequently exists in patients with presbycusis and to describe the effect of cochlear synaptopathy on speech recognition in noise. Based on the medical history and an audiological examination, 94 elderly patients with bilateral, symmetrical, sensorineural hearing loss were diagnosed as presbycusis. An electrocochleogram, auditory brainstem responses, auditory cortical evoked potentials, and speech audiometry were recorded to access the function of the auditory pathway. First, 65 ears with hearing levels of 41-50 dB HL were grouped based on the summating potential/action potential (SP/AP) ratio, and the amplitudes of AP and SP were compared between the two resulting groups. Second, 188 ears were divided into two groups: the normal SP/AP and abnormal SP/AP groups. The speech recognition abilities in the two groups were compared. Finally, the relationship between abnormal electrocochleogram and poor speech recognition  was analyzed in 188 ears. The results of the present study showed: (1) a remarkable reduction in the action potential amplitude was observed in patients with abnormal SP/AP ratios; this suggests that cochlear synaptopathy was involved in presbycusis. (2) There was a large proportion of patients with poor speech recognition in the abnormal SP/AP group. Furthermore, a larger number of cases with abnormal SP/AP ratios were confirmed among patients with presbycusis and poor speech recognition. We concluded that cochlear synaptopathy is not uncommon among elderly individuals who have hearing ability deficits, and it may have a more pronounced effect on ears with declining auditory performance in noisy environments. Presbycusis, also known as age-related hearing loss, is the loss of hearing that gradually occurs in most individuals as they grow older. It can be defined as progressive, bilateral, symmetrical hearing loss ranging from high frequency to speech frequency that impairs an individual's communicative skills . In ChinExperiencing greater levels of difficulty in speech recognition within adverse listening environments is the primary handicapping dysfunction among aging listeners. Previously, several researchers were focused on correlating declining auditory performance with the degeneration of cochlear hair cells. The development of therapeutic interventions was also concentrated on improving speech recognition through signal amplification. As a rule, in hearing aid fitting, when the average hearing thresholds exceeds 40 dB HL on an audiogram, amplification is indispensable. However, only one out of five people who could benefit from using a hearing aid actually wears one. The majority of adults aged between 55 and 74 years who were fitted for a hearing aid do not wear it since there have been complaints regarding the hearing aid not providing sufficient hearing-related benefits in complex listening situations . At the Nevertheless, recent animal studies have revealed that cochlear synaptopathy may be an early-onset contributor to auditory functional decline . CochleaIt is undeniable that hair cell loss, synapsis loss, and neuron degeneration have a significant effect on presbycusis; however, the relative contribution of each of these factors is still unrevealed. Based on the results of animal and human temporal bone studies, we hypothesized that cochlear synaptopathy may greatly contribute to the higher difficulty levels that aging listeners experience in understanding speech in a noisy environment. Therefore, the present study was designed with the aim of identifying the role of cochlear synaptopathy in human presbycusis, which could provide valuable insights that could aid in developing comprehensive strategies to manage presbycusis.Declaration of Helsinki, as reflected in an a priori approval by the Biomedical Research Committee of the Peking University First Hospital (2018-106).A total of 94 patients who were between 60 and 90 years old were recruited from the Outpatient Department of Otorhinolaryngology Head and Neck Surgery of Peking University First Hospital. They were all native speakers (Mandarin) who were in good health with no histories of ear-related concerns, such as otitis media, sudden deafness, and vertigo; no histories of neurologic and cognitive disorders; and no long-term histories of noise exposure. In addition, all the subjects had a junior high school or higher education level and signed an informed consent form. To be eligible, all the participants underwent otoscopic examinations, and pure tone audiometry (PTA), acoustic immittance, ABR, and auditory cortical evoked potential (cAEP) measurements. The inclusion criteria were audiograms that showed bilateral, symmetrical, sensorineural hearing loss and an acoustic immittance of type A in both ears. Participants with remarkably abnormal ABR and cAEP were excluded. Our study and all the protocols used conformed to the Audiometric thresholds were measured using an Interacoustics audiometer. Pure-tone air-conduction thresholds were measured from 250 Hz to 8000 Hz; the hearing threshold was calculated as the average of 500 Hz, 1000 Hz, 2000 Hz, and 4000 Hz. Bone-conduction thresholds were acquired from 250 Hz to 4000 Hz. Tympanograms were obtained using the Maico Middle Ear Analyzer.The sentences that were used in this study to test the participants' hearing abilities were selected from the Mandarin Quick Speech-in-Noise (M-Quick SIN) Test materials . The tesThe subjects were allowed to practice 2-3 sentences. The intensity of the stimulating sound was 30 dB above the hearing threshold of each subject, and the detailed process was outlined to the subject prior to testing.<2 kΩ. The acoustic stimuli were delivered via silicone tubing connected to the earphones. The stimuli of 100 μs-clicks were delivered at 97 dB nHL in an alternating polarity at the rate of 9.1 Hz. Electrical responses were amplified with a 10-3,000 Hz passband filter. Up to 3000 sweeps were averaged with artifact rejection enabled in the software program. The AP and SP peaks were defined through visual inspection by two experienced observers. We use “one point method” to extract AP and SP. SP is defined as the first negative peak and AP is the second negative peak. The graphic illustration is attached to the supplementary material  and a correlation analysis were used to assess the effect of age on the hearing threshold and of the PTA on speech recognition in noise. A F = 16.83, p < 0.001, see A total of 94 subjects (188 ears), including 49 female and 45 male subjects, aged between 60 and 90 years old (the youngest one was 60 years old while the oldest was 89 years old) were enrolled in this study. The results of the ANOVA revealed that the average hearing threshold increases with age and that there was a significant main effect of age on the hearing status  vs. those generated by the cochlear neurons (AP), i.e., the SP/AP ratio, was identified as being normal when the ratio was <34%; otherwise, it was considered abnormal (according to our laboratory standard). We observed that 44% of the total of 188 ears exhibited an abnormal SP/AP. To investigate the cause of the high SP/AP ratios, 65 ears with hearing levels of 41-50 dB HL were selected for further analysis. They were divided into two groups based on the SP/AP ratio: the SP/AP <34% and SP/AP ≥34% groups. The basic characteristics of the 65 ears are presented in t = 4.424, p < 0.001, t = 1.926, p = 0.064, When the AP amplitude was compared between the two groups, a remarkable reduction in the AP amplitude was observed in the group with abnormal SP/AP ratios  and Group B (SNR loss ≥7 dB). An SNR loss of ≥7 dB indicates poor speech recognition ability based on previous research . A largePresbycusis is caused by the combination of multiple physiological factors, including the degeneration of the peripheral auditory organs, effects of noise, systemic diseases, and usage of ototoxic drugs . NumerouWe recruited 94 presbycusis patients (188 ears) who met the study's inclusion criteria. In accordance with the results of previous studies, our study demonstrated that with age, a steady rise occurs in the hearing threshold. In addition, there is a certain negative correlation between the PTA and speech recognition ability in noisy environments.The first question that we sought to clarify in the present study was whether cochlear synaptopathy could potentially be a crucial clinical signature of presbycusis in human beings. The most commonly reported biomarker of synaptopathy is a marked reduction in the ABR wave-I amplitude at suprathreshold stimulus levels , while iAs observed via the scatter plot representing the overall SNR loss and SP/AP ratio, speech recognition is associated with the SP/AP ratio in noisy environments. Some patients with high SP/AP ratios exhibited a poor auditory performance . The secThe current study found that the majority of participants with an enhanced SP/AP ratio were present in the group exhibiting the worse auditory performance; further, the intergroup differences were highly significant. These results may be explained by the fact that cochlear synaptopathy may be a dominant factor involved in the declining auditory performance of elderly subjects.There could be many factors that contribute to the declining auditory recognition in adverse listening situations in aging listeners. Indeed, many longitudinal and cross-sectional studies have demonstrated that the risk of CAPD in the elderly increases with age . Age-relThe current classification of presbycusis based on audiometric tests and temporal bone pathology data was established by Schuknecht in 1993 . HoweverOur study recruited 94 presbycusis patients and observed a remarkable reduction in the AP amplitude in the patients with presbycusis with abnormal SP/AP ratios, which suggests the presence of cochlear synaptopathy in these presbycusis patients. Additionally, there was a large proportion of patients with poor speech recognition in the abnormal SP/AP group. We confirmed that cochlear synaptopathy could potentially be a dominant factor involved in declining auditory performance in noisy environments among patients with presbycusis.


Corticosteroids are one of the anti-inflammatory drugs that are used widely by dermatologists. Significant local adverse effects can happen if topical corticosteroids (TCs) are used incorrectly. This study aimed to assess the prevalence of facial TCs misuse and its adverse effects. This was cross-sectional research: a self-reported questionnaire was distributed among a population of Saudi Arabians aged 16 years and above who were using TCs consecutively. Statistical analysis was performed using SPSS version 26. A total of 611 participants were enrolled in the survey: 401 (65.6%) were university graduates, while 187 (30.6%) were below high school level of education. The number of participants using TCs was 279 (45.7%), while 332 (54.3%) did not use steroids topically. The most used TCs were Mometasone furoate 0.1% cream (18.2%), followed by Fusidic acid/Betamethasone cream (16.7%). A total of 46 reported facial TCs’ side effects. Peeling (52.2%) was the most reported side effect, followed by redness (41.3%). In conclusion, the use of facial TCs among the Saudi population is not uncommon (16.5%). A large population are not aware of the side effects of the unsupervised use of TCs. An effort should be made to increase awareness of the adverse effects of TCs. In 1950, Philip Hench, Edward Kendall, and Tadeus Reichstein discovered the corticosteroid hormone .Topical corticosteroid is the synthetic form of natural corticosteroid which is synthesized in the adrenal cortex . TopicalA community-based, cross-sectional study was conducted. A validated self-report questionnaire was distributed among participants to assess the adverse side effects of using facial TCs. The inclusion criteria were: (1) aged 16 years or above; (2) from Saudi Arabia; (3) male or female. The study proposal was ethically approved by the ethical committee of the scientific research committee at King Fahad Hofuf Hospital. Data collection was conducted by an online questionnaire using Google Forms. The questionnaire was validated after being designed by two expert consultant dermatologists. The next step included a pilot study through distributing the questionnaire to over 25 participants. Then, this process was followed by cleaning the collected data to reduce the risk of error, using principal components analysis, checking the internal consistency, and revising the survey as a final stage. The validated questionnaire was distributed by social media to the Saudi Arabian population. We had volunteers from each region who ensured that the questionnaire was delivered equally to all Saudi regions. The participants had given their consent to participate in the study before filling out the questionnaire. The questionnaire included the demographic data of the participants . Then, it was followed by questions about the usage of TCs.n = z2pq\d2. The confidence level was 95%, the estimated proportion was 50%, with a 5% level of precision. The minimum sample size was calculated to be 385. However, more participants were included to ensure accuracy.The population aged 16 and above who live in Saudi Arabia are 25,828,206, depending on the data available in the General Authority for Statistics of Saudi Arabia . The samp-value less than 0.05 was statistically significant. Descriptive analysis based on frequency and percent distribution was performed for all variables, including participants’ socio-demographic data, facial TCs use, types of used steroids, frequency of use, causes of use, TCs complications, and their satisfaction regarding used topical facial steroids. Cross tabulation was used to assess the distribution of facial TCs use according to their socio-demographic data. Relations were tested using Pearson’s chi-square test and exact probability test for small frequency distributions.Data analysis was performed after data were extracted. It was revised, coded, and fed to statistical software IBM SPSS version 22 . All statistical analysis was conducted using two-tailed tests. A A total of 611 participants fulfilling the inclusion criteria completed the study questionnaire. The participants ranged from 16 to 67 years, with a mean age of 29.1 ± 11.6 years old. An exact number of 494 (80.9%) participants were female. As for educational level, 401 (65.6%) were university graduates, 187 (30.6%) were high school graduates, and (22.3%) were below high school. A total of 267 (43.7%) respondents were students, 136 (22.3%) had governmental jobs, and 160 (26.2%) were not employed. Among participants, 62 (13.7%) were physicians, 22 (4.9%) were nurses, and 9 (2%) were pharmacists. As for the prevalence of using facial steroids, 279 (45.7%) study participants used TCs, while 332 (54.3%) did not use TCs.The most used type of TCs were Mometasone furoate 0.1% cream (18.2%), followed by Fusidic acid/betamethasone cream (16.7%), Hydrocortisone 1% cream (4.4%), and Betamethasone valerate 0.1% cream (0.8%). A total of 41.6% of the facial topical steroids users applied it to cheeks, while 21.5% used TCs on the forehead, 21.1% applied TCs around the mouth, 3.9% around their eyes, while 30.5% applied TCs for the whole face. Regarding the causes of using TCs, 45.9% of the users used for acne, followed by 30.5% for eczema/dermatitis, 14% used TCs to lighten their skin, 5% for freckles, and 5% for psoriasis. Regarding the frequency of using TCs, 35.1% applied TCs once daily, 16.1% applied steroids twice daily, and 7.2% applied every 2 weeks. Exactly 44.1% of TCs users had the cream form, while 52% had ointment, and 3.9% used other forms. Using TCs for 1–2 months was reported among 38% of the users, while 42.3% used topical steroids for more than 9 months. An exact number of 102 (36.6%) participants who used the TCs reported still using it .Nevertheless, 154 (55.2%) users had facial TCs based on a doctor’s prescription. Among those who used unprescribed TCs, family & friends were the main source of advice to use (41.6%), followed by pharmacists (25.6%), social media (18.4%), and books (5.6%). Only 77 (27.6%) TCs users followed up with a dermatologist to note the effect of using TCs on the face. Side effects due to the use of facial TCs were reported among 46 (16.5%) participants. The most reported side effects were peeling (52.2%), followed by redness (41.3%), dryness (23.9%), skin color change (21.7%), irritation (19.6%), acne-like rash (10.9%), and itching (10.9%). A burning sensation was one of the least reported side effects (6.5%), along with skin stretch marks (2.2%). An exact number of 108 (38.7%) knew the side effects of using TCs, and 23 (50%) of those with side effects reported they would return to use TCs if the side effects improved .A total of 93 (33.3%) users were not satisfied, 78 (28%) were moderately satisfied, and 108 (38.7%) TCs users were highly satisfied. A total of 127 (68.3%) steroid users needed less than 1 month to be satisfied, while 39 (21%) needed 1–3 months, but 5 (2.7%) needed more than 9 months. A total of 167 (59.9%) participants did not use anything after they stopped using TCs, while 53 (19%) used another type of cream, and 50 (17.9%) used other methods. As for participants’ observations on their skin after stopping TCs use, 161 (65.7%) observed no change in the treatment result, 65 (26.5%) still had the same problem as before using cortisone, and 12 (4.9%) had persistent side effects .p = 0.001). Moreover, TCs use was recorded among 47.8% of females, in comparison with 36.8% of males (p = 0.031). A total of 51.7% of the students used facial TCs versus 35.4% of workers at non-governmental settings (p = 0.029). Furthermore, facial TCs use was reported among 77.3% of nurses, compared to 43.5% of physicians (p = 0.027)  .Corticosteroids are a type of anti-inflammatory drug that can be prescribed in a systemic or topical form. In 1951, topical corticosteroids (TCs) were used for the first time by American dermatologist Marion Baldur Sulzberger . In modeRegarding the prevalence of using facial TCs among the Saudi population, this study shows that 279 (45%) participants used facial topical steroids, while 332 (54%) did not use facial topical steroids. Topical steroids usage is more popular among females than the male population, which is similar to what was reported in previous studies ,8. Most The most common reason for participants to use facial TCs in this study was treating acne 45.9%). This can be explained by the fact that acne vulgaris is one of the most common inflammatory disorders, affecting a large percentage of the young population in Saudi Arabia (78%) %. This c. LocallyIn this study, the most commonly used form of TCs was ointment in a frequency of once daily for more than nine months, while the average duration in a previous, similar study was around 6 ± 2 months .This study exhibits that 55.2% of the users had facial topical steroids based on doctor prescriptions. This matched the results of another study: 44% of women who used the topical steroids received the prescription from a doctor . Among tBecause the nature of facial skin is thinner than other parts of the body, with a high transcutaneous absorption rate, side effects are more common on the face . NeverthRegarding the satisfaction of TCs use, 38.7% were highly satisfied. Most patients (68.3%) needed less than one month to be satisfied. The majority of our participants (59.9%) did not use anything after they stopped using TCs. However, only a few (3.2%) went to a dermatologist for side effects. The observations on the skin after stopping TCs, 65.7% observed no change in the treatment result.The usage of facial TCs among the Saudi population is not uncommon. Topical facial steroids side effects are not very common in Saudi Arabia (16.5%). A large population are not aware of the side effects of unsupervised usage of TCs. Efforts should be made to increase awareness about the adverse effects of topical TCs.