Multi-pass feature enhancement based on generative-discriminative hybrid approach for noise robust speech recognition

Abstract

This paper presents multi-pass feature enhancement technique that consists of three processing passes. In the proposed method, the first pass was described in our previous work, and consists of model-based feature enhancement realized by employing a generative-discriminative hybrid approach with Gaussian mixture models and deep neural networks (DNNs). As an extension of the previous work, the second pass of the proposed method utilizes DNNs retrained with iterative realignment and auxiliary features obtained from intermediate parameters of the first processing pass. In the third pass, we apply unsupervised DNN adaptation and system combination to the results of the second pass. Therefore, the proposed multi-pass technique realizes stepwise improvements in feature enhancement. For CHiME3 task evaluations, the proposed method provided noticeable improvements in noisy speech recognition accuracy compared with results obtained using the previous one-pass feature enhancement technique.