Bridging the Gap Between Monaural Speech Enhancement and Recognition with Distortion-Independent Acoustic Modeling
TABLE V: WER comparisons between the proposed models and prior work on CHiME-2.
['models', '9dB', '6dB', '3dB', '0dB', '-3dB', '-6dB', 'avg']
For the noise-dependent acoustic model, we achieve an average WER of 8.7%, outperforming the previous best system by 6.5% relatively. Note that, in order to avoid the influence of model adaptation on our analysis of the distortion problem, we do not apply speaker adaptation to our models. The good results of our proposed models suggest that the observations in this study are likely valid for real world systems.