A novel projection-based likelihood measure for noisy speech recognition

[[abstract]]© 1998 Elsevier - The projection-based likelihood measure, an effective means of reducing noise contamination in speech recognition, dynamically searches an optimal equalization factor for adapting the cepstral mean vector of hidden Markov model (HMM) to equalize the noisy observation. In this piper, we present a novel likelihood measure which extends the adaptation mechanism to the shrinkage of covariance matrix and the adaptation bias of mean vector. A set of adaptation functions is proposed for obtaining the compensation factors, Experiments indicate that the likelihood measure proposed herein can markedly elevate the recognition accuracy.[[department]]電機工程學