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In this paper we present a single-microphone speech enhancement algorithm. Ahybrid approach is proposed merging the generative mixture of Gaussians (MoG)model and the discriminative neural network (NN). The proposed algorithm isexecuted in two phases, the training phase, which does not recur, and the testphase. First, the noise-free speech power spectral density (PSD) is modeled asa MoG, representing the phoneme based diversity in the speech signal. An NN isthen trained with phoneme labeled database for phoneme classification withmel-frequency cepstral coefficients (MFCC) as the input features. Given thephoneme classification results, a speech presence probability (SPP) is obtainedusing both the generative and discriminative models. Soft spectral subtractionis then executed while simultaneously, the noise estimation is updated. Thediscriminative NN maintain the continuity of the speech and the generativephoneme-based MoG preserves the speech spectral structure. Extensiveexperimental study using real speech and noise signals is provided. We alsocompare the proposed algorithm with alternative speech enhancement algorithms.We show that we obtain a significant improvement over previous methods in termsof both speech quality measures and speech recognition results.

A Hybrid Approach for Speech Enhancement Using MoG Model and Neural Network Phoneme Classifier