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

TLDR: In this article, the authors proposed a multiple linear regression of the log spectra (MRLS) for estimating the spectra of speech at a close-talking microphone and extended the MRLS concept to nonlinear regressions.

Abstract: 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.