Optimizing regression for in-car speech recognition using multiple distributed microphones

In this paper, we address issues in improving handsfree speech recognition performance in different car environments using multiple spatially distributed microphones. In previous work, we proposed multiple regression of the log-spectra (MRLS) for estimating the logspectra of speech at a close-talking microphone. In this paper, the idea is extended to nonlinear regressions. Isolated word recognition experiments under real car environments show that, compared to the nearest distant microphone, recognition accuracies could be improved by about 40% for very noisy driving conditions by using the optimizing regression method, The proposed approach outperforms linear regression methods and also outperforms adaptive beamformer by 8% and 3% respectively in terms of averaged recognition accuracies.