Speech recognition system robust to noise and speaking styles

Abstract

It is difficult to recognize speech distorted by various factors, especially when an ASR system contains only a single acoustic model. One solution is to use multiple acoustic models, one model for each different condition. In this paper, we discuss a parallel decoding-based ASR system that is robust to the noise type, SNR, speaker gender and speaking style. Our system consists of two recognition channels based on MFCC and Differential MFCC (DMFCC) features. Each channel has several acoustic models depending on SNR, speaker gender and speaking style, and each acoustic model is adapted by fast noise adaptation. From each channel, one hypothesis is selected based on its likelihood. The final recognition result is obtained by combining hypotheses from the two channels. We evaluate the performance of our system by normal and hyperarticulated test speech data contaminated by various types of noise at different SNR levels. Experiments demonstrate that the system could achieve recognition accuracy in excess of 80% for the normal speaking style data at a SNR of 0 dB. For hyper-articulated speech data, the recognition accuracy improved from about 10% to over 45% compared to a system without acoustic models for hyperarticulated speech.