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.

A challenging, unsolved problem in the speech recognition community is recognizing speech signals that are corrupted by loud, highly nonstationary noise. One approach to noisy speech recognition is to automatically remove the noise from the cepstrum sequence 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 noisefree speech from the noisy speech. We showed how an iterative 2nd order vector Taylor series approximation could be used for probabilistic inference in this model. In many circumstances, it is not possible to obtain examples of noise without speech.

A challenging, unsolved problem in the speech recognition community is recognizing speech signals that are corrupted by loud, highly nonstationary noise. One approach to noisy speech recognition is to automatically remove the noise from the cepstrum sequence 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 noisefree speech from the noisy speech. We showed how an iterative 2nd order vector Taylor series approximation could be used for probabilistic inference in this model. In many circumstances, it is not possible to obtain examples of noise without speech.

A challenging, unsolved problem in the speech recognition community isrecognizing speech signals that are corrupted by loud, highly nonstationary noise. One approach to noisy speech recognition isto automatically remove the noise from the cepstrum sequence beforefeeding 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 noisefree speechfrom the noisy speech. We showed how an iterative 2nd order vector Taylor series approximation could be used for probabilistic inferencein this model. In many circumstances, it is not possible to obtain examples of noise without speech.