This paper demonstrates two novel methods to estimate the global SNR of speech signals. In both methods, Deep Neural Network-Hidden Markov Model (DNN-HMM) acoustic model used in speech recognition systems is leveraged for the additional task of SNR estimation. In the first method, the entropy of the DNN-HMM output is computed. Recent work on bayesian deep learning has shown that a DNN-HMM trained with dropout can be used to estimate model uncertainty by approximating it as a deep Gaussian process. In the second method, this approximation is used to obtain model uncertainty estimates. Noise specific regressors are used to predict the SNR from the entropy and model uncertainty. The DNN-HMM is trained on GRID corpus and tested on different noise profiles from the DEMAND noise database at SNR levels ranging from -10 dB to 30 dB.
SNR estimation has been an active area of research. In @cite_8 , the authors use specific handcrafted features such as signal energy, signal variability, pitch and voicing probability to train noise specific regressors that compute SNR of an input signal. In @cite_11 , the amplitude of clean speech is modelled by a gamma distribution and noise is assumed to be normally distributed. SNR is estimated by observing changes to the parameters of the gamma distribution upon addition of noise.
@cite_11 @cite_8
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Abstract In this paper, we introduce a new algorithm for estimating the signal-to-noise ratio (SNR) of speech signals, called WADA-SNR (Waveform Amplitude Distribution Analysis). In this algorithm we assume that the amplitude distribution of clean speech can be approximated by the Gamma distribution with a shaping parameter of 0.4, and that an additive noise signal is Gaussian. Based on this assumption, we can estimate the SNR by examining the amplitude distribution of the noise-corrupted speech. We evaluate the performance of the WADA-SNR algorithm on databases corrupted by white noise, background music, and interfering speech. The WADA-SNR algorithm shows significantly less bias and less variability with respect to the type of noise compared to the standard NIST STNR algorithm. In addition, the algorithm is quite computationally efficient. Index Terms : SNR estimation, Gamma distribution, Gaussian distribution 1. Introduction The estimation of signal-to-noise ratios (SNRs) has been extensively investigated for decades and it is still an active field of research ( Many speech processing algorithms and applications rely on the explicit knowledge of signal-to-noise ratio (SNR) in their design and implementation. Estimating the SNR of a signal can enhance the performance of such technologies. We propose a novel method for estimating the long-term SNR of speech signals based on features, from which we can approximately detect regions of speech presence in a noisy signal. By measuring the energy in these regions, we create sets of energy ratios, from which we train regression models for different types of noise. If the type of noise that corrupts a signal is known, we use the corresponding regression model to estimate the SNR. When the noise is unknown, we use a deep neural network to find the “closest” regression model to estimate the SNR. Evaluations were done based on the TIMIT speech corpus, using noises from the NOISEX-92 noise database. Furthermore, we performed cross-corpora experiments by training on TIMIT and NOISEX-92 and testing on the Wall Street Journal speech corpus and DEMAND noise database. Our results show that our system provides accurate SNR estimations across different noise types, corpora, and that it outperforms other SNR estimation methods.