Robust Speech Recognition Based on Dereverberation Parameter Optimization Using Acoustic Model Likelihood

Abstract

Automatic speech recognition (ASR) in reverberant environments is a challenging task. Most dereverberation techniques address this problem through signal processing and enhances the reverberant waveform independent from the speech recognizer. In this paper, we propose a novel scheme to perform dereverberation in relation with the likelihood of the back-end ASR system. Our proposed approach effectively selects the dereverberation parameters, in the form of multiband scale factors, so that they improve the likelihood of the acoustic model. Then, the acoustic model is retrained using the optimal parameters. During the recognition phase, we implement additional optimization of the parameters. By using Gaussian mixture model (GMM), the process for selecting the scale factors become efficient. Moreover, we remove the dependency of the adopted dereverberation technique on the room impulse response (RIR) measurement, by using an artificial RIR generator and selecting based on the acoustic likelihood. Experimental results show significant improvement in recognition performance with the proposed method over the conventional approach.