This paper describes multichannel speech enhancement for improving automatic speech recognition (ASR) in noisy environments. Recently, the minimum variance distortionless response (MVDR) beamforming has widely been used because it works well if the steering vector of speech and the spatial covariance matrix (SCM) of noise are given. To estimating such spatial information, conventional studies take a supervised approach that classifies each time-frequency (TF) bin into noise or speech by training a deep neural network (DNN). The performance of ASR, however, is degraded in an unknown noisy environment. To solve this problem, we take an unsupervised approach that decomposes each TF bin into the sum of speech and noise by using multichannel nonnegative matrix factorization (MNMF). This enables us to accurately estimate the SCMs of speech and noise not from observed noisy mixtures but from separated speech and noise components. In this paper, we propose online MVDR beamforming by effectively initializing and incrementally updating the parameters of MNMF. Another main contribution is to comprehensively investigate the performances of ASR obtained by various types of spatial filters, i.e., time-invariant and variant versions of MVDR beamformers and those of rank-1 and full-rank multichannel Wiener filters, in combination with MNMF. The experimental results showed that the proposed method outperformed the state-of-the-art DNN-based beamforming method in unknown environments that did not match training data.
TF Mask estimation has actively been studied for computing the SCMs of speech and noise @cite_25 @cite_18 @cite_23 @cite_11 @cite_24 @cite_28 @cite_40 @cite_49 . Our unsupervised method is different from DNN-based mask estimation @cite_18 @cite_23 @cite_11 @cite_24 @cite_28 @cite_40 @cite_49 in two ways. First, our method decomposes each TF bin into the sum of speech and noise, while the mask-based methods calculate the SCM of speech from noisy TF bins without any decomposition. Second, our method uses no training data, while in general the DNN-based methods need a sufficient number of pairs of noisy spectrograms and ideal binary masks (IBMs). The performance of the DNN-based mask estimation would be degraded in unseen conditions that are not covered by the training data because of overfitting to the training data.
@cite_18 @cite_28 @cite_24 @cite_40 @cite_23 @cite_49 @cite_25 @cite_11
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Recently, time-frequency mask-based beamforming has been extensively studied as the frontend of deep neural network (DNN) based automatic speech recognition (ASR) in noisy environments. Two mask estimation approaches have been separately developed for this beamforming method, namely the the DNN-based approach, which exploits the time-frequency features of the signal, and the spatial clustering-based approach, which exploits the spatial features of the signal. This paper proposes a new method that integrates the two approaches in a probabilistic way to further improve mask estimation by exploiting the advantages of both approaches. Experiments using the real data of the CHiME-3 multichannel noisy speech corpus show that the proposed method almost always outperforms the conventional approaches in terms of word error rate (WER) improvement. Acoustic beamforming has played a key role in the robust automatic speech recognition (ASR) applications. Accurate estimates of the speech and noise spatial covariance matrices (SCM) are crucial for successfully applying the minimum variance distortionless response (MVDR) beamforming. Reliable estimation of time-frequency (TF) masks can improve the estimation of the SCMs and significantly improve the performance of the MVDR beamforming in ASR tasks. In this paper, we focus on the TF mask estimation using recurrent neural networks (RNN). Specifically, our methods include training the RNN to estimate the speech and noise masks independently, training the RNN to minimize the ASR cost function directly, and performing multiple passes to iteratively improve the mask estimation. The proposed methods are evaluated individually and overally on the CHiME-4 challenge. The results show that the proposed methods improve the ASR performance individually and also work complementarily. The overall performance achieves a word error rate of 8.9 with 6-microphone configuration, which is much better than 12.0 achieved with the state-of-the-art MVDR implementation. We present a neural network based approach to acoustic beamforming. The network is used to estimate spectral masks from which the Cross-Power Spectral Density matrices of speech and noise are estimated, which in turn are used to compute the beamformer coefficients. The network training is independent of the number and the geometric configuration of the microphones. We further show that it is possible to train the network on clean speech only, avoiding the need for stereo data with separated speech and noise. Two types of networks are evaluated. One small feed-forward network with only one hidden layer and one more elaborated bi-directional Long Short-Term Memory network. We compare our system with different parametric approaches to mask estimation and using different beamforming algorithms. We show that our system yields superior results, both in terms of perceptual speech quality and with respect to speech recognition error rate. The results for the simple feed-forward network are especially encouraging considering its low computational requirements. The recently-proposed deep clustering algorithm represents a fundamental advance towards solving the cocktail party problem in the single-channel case. When multiple microphones are available, spatial information can be leveraged to differentiate signals from different directions. This study combines spectral and spatial features in a deep clustering framework so that the complementary spectral and spatial information can be simultaneously exploited to improve speech separation. We find that simply encoding inter-microphone phase patterns as additional input features during deep clustering provides a significant improvement in separation performance, even with random microphone array geometry. Experiments on a spatial-ized version of the wsj0-2mix dataset show the strong potential of the proposed algorithm for speech separation in reverberant environments. This paper considers acoustic beamforming for noise robust automatic speech recognition. A beamformer attenuates background noise by enhancing sound components coming from a direction specified by a steering vector. Hence, accurate steering vector estimation is paramount for successful noise reduction. Recently, time-frequency masking has been proposed to estimate the steering vectors that are used for a beamformer. In particular, we have developed a new form of this approach, which uses a speech spectral model based on a complex Gaussian mixture model CGMM to estimate the time-frequency masks needed for steering vector estimation, and extended the CGMM-based beamformer to an online speech enhancement scenario. Our previous experiments showed that the proposed CGMM-based approach outperforms a recently proposed mask estimator based on a Watson mixture model and the baseline speech enhancement system of the CHiME-3 challenge. This paper provides additional experimental results for our online processing, which achieves performance comparable to that of batch processing with a suitable block-batch size. This online version reduces the CHiME-3 word error rate WER on the evaluation set from 8.37 to 8.06 . Moreover, in this paper, we introduce a probabilistic prior distribution for a spatial correlation matrix a CGMM parameter, which enables more stable steering vector estimation in the presence of interfering speakers. In practice, the performance of the proposed online beamformer degrades with observations that contain only noise or and interference because of the failure of the CGMM parameter estimation. The introduced spatial prior enables the target speaker's parameter to avoid overfitting to noise or and interference. Experimental results show that the spatial prior reduces the WER from 38.4 to 29.2 in a conversation recognition task compared with the CGMM-based approach without the prior, and outperforms a conventional online speech enhancement approach.