Adaptive Nonlinear Regression Using Multiple Distributed Microphones for In-Car Speech Recognition

Weifeng LI  Chiyomi MIYAJIMA  Takanori NISHINO  Katsunobu ITOU  Kazuya TAKEDA  Fumitada ITAKURA  

Publication
IEICE TRANSACTIONS on Fundamentals of Electronics, Communications and Computer Sciences   Vol.E88-A   No.7   pp.1716-1723
Publication Date: 2005/07/01
Online ISSN: 
DOI: 10.1093/ietfec/e88-a.7.1716
Print ISSN: 0916-8508
Type of Manuscript: Special Section PAPER (Special Section on Multi-channel Acoustic Signal Processing)
Category: Speech Enhancement
Keyword: 
speech recognition,  support vector machine,  multi-layer perceptron,  signal-to-deviation ratio,  K-means clustering,  adaptive beamforming,  

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Summary: 
In this paper, we address issues in improving hands-free speech recognition performance in different car environments using multiple spatially distributed microphones. In the previous work, we proposed the multiple linear regression of the log spectra (MRLS) for estimating the log spectra of speech at a close-talking microphone. In this paper, the concept is extended to nonlinear regressions. Regressions in the cepstrum domain are also investigated. An effective algorithm is developed to adapt the regression weights automatically to different noise environments. Compared to the nearest distant microphone and adaptive beamformer (Generalized Sidelobe Canceller), the proposed adaptive nonlinear regression approach shows an advantage in the average relative word error rate (WER) reductions of 58.5% and 10.3%, respectively, for isolated word recognition under 15 real car environments.