Rank-1 Constrained Multichannel Wiener Filter for Speech Recognition in Noisy Environments

Multichannel linear filters, such as the Multichannel Wiener Filter (MWF) and the Generalized Eigenvalue (GEV) beamformer are popular signal processing techniques which can improve speech recognition performance. In this paper, we present an experimental study on these linear filters in a specific speech recognition task, namely the CHiME-4 challenge, which features real recordings in multiple noisy environments. Specifically, the rank-1 MWF is employed for noise reduction and a new constant residual noise power constraint is derived which enhances the recognition performance. To fulfill the underlying rank-1 assumption, the speech covariance matrix is reconstructed based on eigenvectors or generalized eigenvectors. Then the rank-1 constrained MWF is evaluated with alternative multichannel linear filters under the same framework, which involves a Bidirectional Long Short-Term Memory (BLSTM) network for mask estimation. The proposed filter outperforms alternative ones, leading to a 40% relative Word Error Rate (WER) reduction compared with the baseline Weighted Delay and Sum (WDAS) beamformer on the real test set, and a 15% relative WER reduction compared with the GEV-BAN method. The results also suggest that the speech recognition accuracy correlates more with the Mel-frequency cepstral coefficients (MFCC) feature variance than with the noise reduction or the speech distortion level.Comment: for Computer Speech and Languag

A Speech Distortion and Interference Rejection Constraint Beamformer

Signals captured by a set of microphones in a speech communication system are mixtures of desired and undesired signals and ambient noise. Existing beamformers can be divided into those that preserve or distort the desired signal. Beamformers that preserve the desired signal are, for example, the linearly constrained minimum variance (LCMV) beamformer that is supposed, ideally, to reject the undesired signal and reduce the ambient noise power, and the minimum variance distortionless response (MVDR) beamformer that reduces the interference-plus-noise power. The multichannel Wiener filter, on the other hand, reduces the interference-plus-noise power without preserving the desired signal. In this paper, a speech distortion and interference rejection constraint (SDIRC) beamformer is derived that minimizes the ambient noise power subject to specific constraints that allow a tradeoff between speech distortion and interference-plus-noise reduction on the one hand, and undesire d signal and ambient noise reductions on the other hand. Closed-form expressions for the performance measures of the SDIRC beamformer are derived and the relations to the aforementioned beamformers are derived. The performance evaluation demonstrates the tradeoffs that can be made using the SDIRC beamformer

Assistive listening headsets for high noise environments: Protection and communication

© 2015 IEEE. In industrial noise environments, the use of assistive listening headsets is a means to provide adequate access to voice communication while wearing hearing protection. This paper presents a performance evaluation and comparison of two different methods to provide the binaural speech enhancement in real industrial noise scenarios. The investigated binaural methods based on differential beamforming and multichannel Wiener filter show different strengths and weaknesses. A transient noise suppression algorithm is also proposed and evaluated. Performance evaluation shows that this algorithm, together with the binaural multi-channel Wiener filter approach, can successfully reduce the hammering noise. This can be observed from the PESQ scores and the signal characteristics

Effective Binaural Multi-Channel Processing Algorithm for Improved Environmental Presence

Binaural noise-reduction algorithms based on multi-channel Wiener filter (MWF) are promising techniques to be used in binaural assistive listening devices. The real-time implementation of the existing binaural MWF methods, however, involves challenges to increase the amount of noise reduction without imposing speech distortion, and at the same time preserving the binaural cues of both speech and noise components. Although significant efforts have been made in the literature, most developed methods so far have focused only on either the former or latter problem. This paper proposes an alternative binaural MWF algorithm that incorporates the non-stationarity of the signal components into the framework. The main objective is to design an algorithm that would be able to select the sources that are present in the environment. To achieve this, a modified speech presence probability (SPP) and a single-channel speech enhancement algorithm are utilized in the formulation. The resulting optimal filter also avoids the poor estimation of the second-order clean speech statistics, which is normally done by simple subtraction. Theoretical analysis and performance evaluation using realistic recorded data shows the advantage of the proposed method over the reference MWF solution in terms of the binaural cues preservation, as well as the noise reduction and speech distortion