Microphone Array Post-filter based on Noise Field Coherence

This article introduces a novel technique for estimating the signal power spectral density to be used in the transfer function of a microphone array post-filter. The technique is a generalisation of the existing Zelinski post-filter, which uses the auto- and cross-spectral densities of the array inputs to estimate the signal and noise spectral densities. The Zelinski technique, however, assumes zero cross-correlation between the noise on different sensors. This assumption is inaccurate, particularly at low frequencies and for arrays with closely spaced sensors, and thus the corresponding post-filter is sub-optimal in realistic noise conditions. In this article, a more general expression of the post-filter estimation is developed based on an assumed knowledge of the complex coherence of the noise field. This general expression can be used to construct a more appropriate post-filter in a variety of different noise fields. In experiments using real noise recordings from a computer office, the modified post-filter results in significant improvement in terms of objective speech quality measures and speech recognition performance using a diffuse noise model

Continuous Microphone Array Speech Recognition on Wall Street Journal Corpus

In this paper, we present a robust speech acquisition system to acquire continuous speech using a microphone array. A microphone array based speech recognition system is also presented to study the environmental interference due to reverberation, background noises and mismatch between the training and testing conditions. This is important in the context of smart meeting rooms of Augmented MultiParty Interaction (AMI) project which aims at significant development of conversational speech recognition. In this regard, an audio-visual database containing the Wall Street journal phrases was recorded in a real meeting room for the stationary speaker, moving speaker and overlapping speech scenarios. We carried out speech enhancement and continuous speech recognition experiments on stationary speaker data. Using a microphone array with beamformer followed by a postfilter enhances speech quality slightly inferior to that of close-talk headset,and better than lapel. We achieved a significant reduction in word error rates using models adapted based on maximum linear likelihood regression (MLLR) and maximum-a-posteriori (MAP) approaches. Though the error rates of the microphone array data are larger than those of headset data, they are significantly smaller compared to the error rates of lapel data

Microphone Array Beampattern Characterization for Hands-free Speech Applications

Spatial filtering is the fundamental characteristic of microphone array based signal acquisition, which plays an important role in applications such as speech enhancement and distant speech recognition. In the array processing literature, this property is formulated upon beam-pattern steering and it is characterized for narrowband signals. This paper proposes to characterize the microphone array broadband beam-pattern based on the average output of a steered beamformer for a broadband spectrum. Relying on this characterization, we derive the directivity beam-pattern of delayand- sum and superdirective beamformers for a linear as well as a circular microphone array. We further investigate how the broadband beam-pattern is linked to speech recognition feature extraction; hence, it can be used to evaluate distant speech recognition performance. The proposed theory is demonstrated with experiments on real data recording