Subband parameter optimization of microphone arrays for speech recognition in reverberant environments

We present a new subband microphone array processing algorithm specifically designed for speech recognition applications. We previously proposed a speech recognizer-based array processing algorithm which resulted in significant improvements in recognition accuracy when the speech was corrupted by additive noise and moderate levels of reverberation. However, little improvement was achieved over conventional beamforming methods in highly reverberant environments. Subband processing has been used to improve the poor performance of LMS-type algorithms when the number of filter parameters to estimate is large and the noise is highly correlated to the speech signal, e.g. in highly reverberant environments. We apply a subband approach to a new array processing architecture in which select groups of subbands are processed jointly to maximize the likelihood of the resulting speech recognition features, as measured by the recognition system itself. By incorporating the recognizer into the filter optimization scheme we ensure that signal components important for recognition are emphasized without undue emphasis on less critical components. By utilizing a subband approach, we can effectively apply this framework to highly reverberant environments. In doing so, we are able to achieve improvements in word error rate of over 20% compared to conventional methods in highly reverberant environments.