Markov Model Based Phoneme Class Partitioning for ImprovedConstrained Iterative Speech
نویسنده
چکیده
Research has shown that degrading acoustic background noise innuences speech quality across phoneme classes in a non-uniform manner. This results in variable quality performance of many speech enhancement algorithms in noisy environments. A phoneme classiication procedure is proposed which directs single-channel constrained speech enhancement. The procedure performs broad phoneme class partitioning of noisy speech frames using a continuous mixture hidden Markov model recognizer in conjunction with a perceptually motivated cost-based decision process. Once noisy speech frames are identiied, iterative speech enhancement based on all-pole parameter estimation with inter-and intra-frame spectral constraints is employed. The phoneme class directed enhancement algorithm is evaluated using TIMIT speech data and shown to result in substantial improvement in objective speech quality over a range of signal-to-noise ratios and individual phoneme classes.
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Minimum cost based phoneme class detection for improved iterative speech enhancement
It is known that degrading acoustic noise innuences speech quality across phoneme classes in a non-uniform manner. This results in variable quality performance for many speech enhancement algorithms in noisy environments. To address this, a hidden-Markov-model phoneme classiica-tion procedure is proposed which directs single channel speech enhancement across individual phoneme classes. The proc...
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