Adaptive beamforming and adaptive training of DNN acoustic models for enhanced multichannel noisy speech recognition
Alexey Alexandrovich Prudnikov, Maxim L. Korenevsky, Sergei Aleinik · 2015
This paper describes our contribution to the development of an ASR system for the CHiME 2015 Challenge. We applied a new adaptive beamforming method of multichannel alignment for enhancing speech recorded with six microphones. Then we trained an effective CD-DNN-HMM acoustic model using CMVN for noise robustness as well as fMLLR and i-vectors for speaker and environment adaptation. As a result, our system provides 7.33% WER on the development set and 14.34% WER on the test set (58% WER reduction compared to the baseline system).