A Hybrid Approach for Speech Enhancement Using MoG Model and Neural Network Phoneme Classifier
This work addresses speech enhancement for applications like communication systems, but it is incremental as it combines existing generative and discriminative techniques.
The paper tackles single-microphone speech enhancement by proposing a hybrid algorithm that merges a mixture of Gaussians model with a neural network phoneme classifier, resulting in significant improvements in speech quality and recognition metrics over previous methods.
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.