Channel selection by class separability measures for automatic transcriptions on distant microphones

Channel selection is important for automatic speech recognition as the signal quality of one channel might be significantly better than those of the other channels and therefore, microphone array or blind source separation techniques might not lead to improvements over the best single microphone. The mayor challenge, however, is to find this particular channel who is leading to the most accurate classification. In this paper we present a novel channel selection method, based on class separability, to improve multi-source far distance speech-totext transcriptions. Class separability measures have the advantage, compared to other methods such as the signal to noise ratio (SNR), that they are able to evaluate the channel quality on the actual features of the recognition system. We have evaluated on NISTs RT-07 development set and observe significant improvements in word accuracy over SNR based channel selection methods. We have also used this technique in NISTs RT-07 evaluation.