Broad phoneme class based speech enhancement using mixture maximum model

This study develops a speech enhancement technique that uses a series of prior enhanced speech utterances, each optimized for a specific broad phoneme class, to generate a single, composite utterance to improve objective quality scores over all phoneme classes. The noisy utterance is partitioned into phoneme class segments using probabilistic decisions made from the mixture maximum model (MIXMAX). Based on these phoneme class decisions, the composite segment is constructed using a combination of the prior enhanced utterances. The enhancement system that generates multiple enhanced utterances is assumed to belong to the class of short-time spectral magnitude estimators which either minimizes the weighted Euclidean distortion (WED) between clean speech and clean speech estimate spectral magnitudes or which finds the joint MAP(JMAP) estimate of clean speech spectral magnitude and phase. Performance evaluations of the composite utterance exhibit better performance than the individual utterances over all phoneme classes in most cases of the noise types and SNR levels considered.