Two-stage Deep Learning for Noisy-reverberant Speech Enhancement

A Survey on Low-Latency DNN-Based Speech Enhancement

This paper presents recent advances in low-latency, single-channel, deep neural network-based speech enhancement systems. The sources of latency and their acceptable values in different applications are described. This is followed by an analysis of the constraints imposed on neural network architectures. Specifically, the causal units used in deep neural networks are presented and discussed in the context of their properties, such as the number of parameters, the receptive field, and computational complexity. This is followed by a discussion of techniques used to reduce the computational complexity and memory requirements of the neural networks used in this task. Finally, the techniques used by the winners of the latest speech enhancement challenges (DNS, Clarity) are shown and compared.

Neural Cascade Architecture with Triple-domain Loss for Speech Enhancement

This paper proposes a neural cascade architecture to address the monaural speech enhancement problem. The cascade architecture is composed of three modules which optimize in turn enhanced speech with respect to the magnitude spectrogram, the time-domain signal and the complex spectrogram. Each module takes as input the noisy speech and the output obtained from the previous module, and generates a prediction of the respective target. Our model is trained in an end-to-end manner, using a triple-domain loss function that accounts for three domains of signal representation. Experimental results on the WSJ0 SI-84 corpus show that the proposed model outperforms other strong speech enhancement baselines in terms of objective speech quality and intelligibility.

A two-stage deep learning algorithm for talker-independent speaker separation in reverberant conditions

Speaker separation is a special case of speech separation, in which the mixture signal comprises two or more speakers. Many talker-independent speaker separation methods have been introduced in recent years to address this problem in anechoic conditions. To consider more realistic environments, this paper investigates talker-independent speaker separation in reverberant conditions. To effectively deal with speaker separation and speech dereverberation, extending the deep computational auditory scene analysis (CASA) approach to a two-stage system is proposed. In this method, reverberant utterances are first separated and separated utterances are then dereverberated. The proposed two-stage deep CASA system significantly outperforms a baseline one-stage deep CASA method in real reverberant conditions. The proposed system has superior separation performance at the frame level and higher accuracy in assigning separated frames to individual speakers. The proposed system successfully generalizes to an unseen speech corpus and exhibits similar performance to a talker-dependent system.

On Cross-Corpus Generalization of Deep Learning Based Speech Enhancement

In recent years, supervised approaches using deep neural networks (DNNs) have become the mainstream for speech enhancement. It has been established that DNNs generalize well to untrained noises and speakers if trained using a large number of noises and speakers. However, we find that DNNs fail to generalize to new speech corpora in low signal-to-noise ratio (SNR) conditions. In this work, we establish that the lack of generalization is mainly due to the channel mismatch, i.e. different recording conditions between the trained and untrained corpus. Additionally, we observe that traditional channel normalization techniques are not effective in improving cross-corpus generalization. Further, we evaluate publicly available datasets that are promising for generalization. We find one particular corpus to be significantly better than others. Finally, we find that using a smaller frame shift in short-time processing of speech can significantly improve cross-corpus generalization. The proposed techniques to address cross-corpus generalization include channel normalization, better training corpus, and smaller frame shift in short-time Fourier transform (STFT). These techniques together improve the objective intelligibility and quality scores on untrained corpora significantly.

Evaluation of Mixed Deep Neural Networks for Reverberant Speech Enhancement

Speech signals are degraded in real-life environments, as a product of background noise or other factors. The processing of such signals for voice recognition and voice analysis systems presents important challenges. One of the conditions that make adverse quality difficult to handle in those systems is reverberation, produced by sound wave reflections that travel from the source to the microphone in multiple directions. To enhance signals in such adverse conditions, several deep learning-based methods have been proposed and proven to be effective. Recently, recurrent neural networks, especially those with long short-term memory (LSTM), have presented surprising results in tasks related to time-dependent processing of signals, such as speech. One of the most challenging aspects of LSTM networks is the high computational cost of the training procedure, which has limited extended experimentation in several cases. In this work, we present a proposal to evaluate the hybrid models of neural networks to learn different reverberation conditions without any previous information. The results show that some combinations of LSTM and perceptron layers produce good results in comparison to those from pure LSTM networks, given a fixed number of layers. The evaluation was made based on quality measurements of the signal’s spectrum, the training time of the networks, and statistical validation of results. In total, 120 artificial neural networks of eight different types were trained and compared. The results help to affirm the fact that hybrid networks represent an important solution for speech signal enhancement, given that reduction in training time is on the order of 30%, in processes that can normally take several days or weeks, depending on the amount of data. The results also present advantages in efficiency, but without a significant drop in quality.

Deep Learning for Talker-dependent Reverberant Speaker Separation: An Empirical Study

Speaker separation refers to the problem of separating speech signals from a mixture of simultaneous speakers. Previous studies are limited to addressing the speaker separation problem in anechoic conditions. This paper addresses the problem of talker-dependent speaker separation in reverberant conditions, which are characteristic of real-world environments. We employ recurrent neural networks with bidirectional long short-term memory (BLSTM) to separate and dereverberate the target speech signal. We propose two-stage networks to effectively deal with both speaker separation and speech dereverberation. In the two-stage model, the first stage separates and dereverberates two-talker mixtures and the second stage further enhances the separated target signal. We have extensively evaluated the two-stage architecture, and our empirical results demonstrate large improvements over unprocessed mixtures and clear performance gain over single-stage networks in a wide range of target-to-interferer ratios and reverberation times in simulated as well as recorded rooms. Moreover, we show that time-frequency masking yields better performance than spectral mapping for reverberant speaker separation.