Feature enhancement based on generative-discriminative hybrid approach with gmms and DNNS for noise robust speech recognition

Abstract

This paper presents a technique that combines generative and discriminative approaches with Gaussian mixture models (GMMs) and deep neural networks (DNNs) for model-based feature enhancement. Typical model-based feature enhancement employs a generative model approach. The enhanced features are obtained by using the weighted sum of linear transformations given by each Gaussian component contained in GMMs and corresponding posterior probabilities. The computation of posterior probabilities is a crucial factor for this kind of feature enhancement, and can also be formulated as the class discrimination problem of observed noisy features. The prominent discriminability of DNNs is a well-known solution to this discrimination problem. Therefore, we propose the use of DNNs for computing posterior probabilities. The proposed method incorporates the benefit of the discriminative approach into the generative approach. For AURORA2 task evaluations, the proposed method provided noticeable improvements compared with results obtained using the conventional generative model approach.