An advanced feature compensation method employing acoustic model with phonetically constrained structure

Abstract

This study proposes an effective model-based feature compensation method for robust speech recognition in background noise conditions. In the proposed scheme, an acoustic model with a phonetically constrained structure is employed for the Parallel Combined Gaussian Mixture Model (PCGMM [1]) based feature compensation method. The structure of the acoustic model includes a collection of context independent phone models. A phonetically constrained prior probability is formulated by integrating transition probability of phone models into the reconstruction procedure. Experimental results show that the PCGMM-based feature compensation employing the proposed phonetically constrained structure of acoustic model consistently outperforms the case of employing the conventional Gaussian mixture model. This demonstrates that the proposed configuration of the acoustic model is effective at improving the intelligibility of the speech reconstructed by the feature compensation method for speech recognition under diverse background noise conditions.