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