Temporal and Spectral Processing Methods for Processing of Degraded Speech: A Review <s> Speech-Specific Approaches <s> A common type of interference in speech transmission is that caused by the speech of a competing talker. Although the brain is adept at clarifying such speech, it relies heavily on binaural data. When voices interfere over a single channel, separation is much more difficult and intelligibility suffers. Clarifying such speech is a complex and varied problem whose nature changes with the moment‐to‐moment variation in the types of sound which interfere. This paper describes an attack on the principal subproblem, the separation of vocalic speech. Separation is done by selecting the harmonics of the desired voice in the Fourier transform of the input. In implementing this process, techniques have been developed for resolving overlapping spectrum components, for determining pitches of both talkers, and for assuring consistent separation. These techniques are described, their performance on test utterances is summarized, and the possibility of using this process as a basis for the solution of the general two‐tal... <s> BIB001 </s> Temporal and Spectral Processing Methods for Processing of Degraded Speech: A Review <s> Speech-Specific Approaches <s> The technique fits a sinusoidal model to additive vocalic speech segments such that the least mean square error between the model and the summed waveforms is obtained. Enhancement is achieved by synthesizing a waveform from the sine waves attributed to the desired speaker. Least squares estimation is applied to obtain sine-wave amplitudes and phases for both talkers, based on either a priori sine-wave frequencies or a priori fundamental frequency contours. When the frequencies of the two waveforms are closely spaced, the performance is significantly improved by exploiting the time evolution of the sinusoidal parameters across multiple analysis frames. The least squares error approach is also extended to estimate fundamental frequency contours of both speakers from the summed waveform. The results obtained, though limited in their scope, provide evidence that the sinusoidal analysis/synthesis model with effective parameter estimation techniques offers a promising approach to the problem of cochannel talker interference suppression over a range of conditions. > <s> BIB002 </s> Temporal and Spectral Processing Methods for Processing of Degraded Speech: A Review <s> Speech-Specific Approaches <s> This paper presents a system for separating the cochannel speech of two talkers. The proposed harmonic enhancement and suppression (HES) system is based on a frame-by-frame speaker separation algorithm that exploits the pitch estimate of the stronger talker derived from the cochannel signal. The idea behind this approach is to recover the stronger talker's speech by enhancing their harmonic frequencies and formants given a multiresolution pitch estimate. The weaker talker's speech is obtained from the residual signal created when the harmonics and formants of the stronger talker are suppressed. An automatic speaker assignment algorithm is used to place recovered frames from the target and interfering talkers in separate channels. Automatic speaker assignment performs reasonably well in most cochannel environments, including voiced-on-voiced, voiced-on-unvoiced, unvoiced-on-unvoiced, assignment after processing silence intervals, and single talker speech (no cochannel interference). The HES system has been tested at target-to-interferer ratios (TIRs) from -18 to 18 dB with widely available data bases. It has demonstrated improved performance in keyword spotting tests for TIR values of 6, 12, and 18 dB, and in human listening tests for TIR values of -6 and -18 dB. <s> BIB003
Initial work in co-channel speaker separation evolved from speech enhancement algorithms designed for separating voiced speech from background noise, given a pitch estimate from the target talker. Pearson BIB001 proposed a harmonic selection method for co-channel speech separation. In this method, first the spectral peaks are identified from the windowed mixed speech spectrum. The peaks are accumulated in a table that is used to construct a histogram. The pitch (F0) of a first speaker is determined from the histogram and the F0 of the second speaker is obtained by removing the harmonics belonging to the first speaker from the peak table and repeating the histogram calculation for remaining peaks. The speech of each speaker is then resynthesized by taking inverse discrete Fourier transform (IDFT) of separated pitch and harmonics. Morgan et al. BIB003 proposed a harmonic enhancement and suppression algorithm for separating the two speakers. The idea was to recover the stronger speaker's speech by enhancing his/her harmonics and formants, given a multi resolution pitch estimate. The weaker speaker's speech is then obtained from the residual signal created, when the harmonics and formants of the stronger talker are suppressed. When there are more than two talkers in the co-channel signal, only the stronger speaker can be separated, and the separation is predicated on the basis that the speaker is always stronger and voiced. Sinusoidal modeling of speech is also suggested to obtain the co-channel speaker separation BIB002 . The enhancement is achieved by synthesizing a waveform from the sine waves of desired speaker with the help of a priori sine wave frequencies or a priori pitch contour and least square estimation technique. The basic requirement of all these methods is that the voices to be separated must be periodic. Generally the separation of unvoiced speech is more difficult, as compared to voiced speech. This is mainly because of two reasons. Firstly, unvoiced speech lacks harmonic structure and is often acoustically noiselike. Secondly, the energy of unvoiced speech is usually much weaker than that of voiced speech. However, by the nature of speech production, most of the speech produced is of the voiced type, and, hence, nearly all the information is perceived from the voiced sounds itself.