Adaptation to new microphones using tied-mixture normalization

In this paper, we present several approaches designed to increase the robustness of BYBLOS, the BBN continuous speech, hidden Markov model (HMM) recognition system. We address the problem of increased degradation in performance when there is mismatch in the characteristics of the training and the test microphones. First we compare RASTA processing and cepstrum mean subtraction as preprocessing methods, to compensate for unknown channel transfer function effects, when we have no information about the new microphone. Then we introduce a new algorithm that computes a probabilistic transformation from the training microphone codebook to that of a new microphone, given some information about the new microphone. We test this algorithm in supervised mode and, combined with a microphone selection method, in unsupervised mode. We present experimental results which show that the proposed algorithm combined with cepstrum mean subtraction, improves the recognition accuracy when the system is tested on a microphone with different characteristics than the one on which it was trained