How to utilize the time correlation of speech/nonspeech presence is a crucial problem faced by noise estimators. The popular technique of exploiting such correlation is to smooth noisy spectra by using a temporal recursive filter with a time-varying smoothing factor. But this technique cannot warrant the statistical optimality. In theory, hidden Markov model (HMM) is more desirable than this technique. It can give an elaborate description of speech/nonspeech transition. Moreover, some theoretical frameworks, such as maximum likelihood (ML), are available for optimal estimation. This paper presents a constrained sequential HMM to model the time correlation of speech/nonspeech presence of an individual log-power sequence. Its parameter set is on-line adapted to varying signals based on a ML framework. We compared its performance with that of well-established algorithms by speech enhancement experiments. The results confirmed its promising performance.
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