Feature compensation employing online GMM adaptation for speech recognition in unknown severely adverse environments

This study proposes an effective feature compensation-method to improve speech recognition in real-life speech conditions, where (i) severe background noise and channel distortion simultaneously exist, (ii) no development data is available, and (iii) clean data for ASR training and the latent clean speech in the test data are mismatched in the acoustic structure. The proposed feature compensation method employs an online GMM adaptation procedure which is based on MLLR, and a minimum statistics replacement technique for non-speech segments. The DARPA Tank corpus is used for performance evaluation, which includes severe real-life noisy conditions. The clean Broadcast News (BN) corpus is used for training the speech recognition system in this study. Experimental results show that the proposed feature compensation scheme outperforms GMM-based FMLLR and the ETSI AFE for DARPA Tank data, achieving a +5.56% relative improvement compared to FMLLR. These results demonstrate that the proposed feature compensation scheme is effective at improving speech recognition performance in unknown real-life adverse environments.