If speech is captured by several arbitrarily-located microphones in a room, the degree of distortion by noise and reverberation may vary strongly from one channel to another. Channel selection for automatic speech recognition aims to rank the signals according to their quality, and, in particular, to select the best one for further processing in the recognition system. To create this ranking, we propose here to use posterior probabilities estimated from the N-best hypothesis of each channel. When evaluated experimentally, this new channel selection technique outperforms the methods published so far. We also propose the combination of different channel selection techniques to further increase the recognition accuracy and to reduce the computational load without significant performance loss.

Peer Reviewed