ALGONQUIN - Learning Dynamic Noise Models From Noisy Speech for Robust Speech Recognition

A challenging, unsolved problem in the speech recognition com(cid:173) munity is recognizing speech signals that are corrupted by loud, highly nonstationary noise. One approach to noisy speech recog(cid:173) nition is to automatically remove the noise from the cepstrum se(cid:173) quence before feeding it in to a clean speech recognizer. In previous work published in Eurospeech, we showed how a probability model trained on clean speech and a separate probability model trained on noise could be combined for the purpose of estimating the noise(cid:173) free speech from the noisy speech. We showed how an iterative 2nd order vector Taylor series approximation could be used for prob(cid:173) abilistic inference in this model. In many circumstances, it is not possible to obtain examples of noise without speech.