Adaptive log-spectral regression for in-car speech recognition using multiple distributed microphones

This letter addresses issues in improving hands-free speech recognition performance in different car environments. We propose a new speech-enhancement approach based on optimizing regression of the log-spectra, which is used to estimate the log-spectra of speech at a close-talking microphone by using multiple spatially distributed microphones. The regression weights can be adapted automatically for different noise environments. Compared to the nearest distant microphone and adaptive beamformer generalized sidelobe canceller (GSC), the proposed approach shows an advantage in the average relative word error rate (WER) reduction of 58.5 and 10.3%, respectively, for isolated word recognition under 15 real-car environments.