Multichannel End-to-end Speech Recognition
Table 2: Character error rate [%] for AMI corpus.
['model', 'dev', 'eval']
To further investigate the effectiveness of our proposed multichannel end-to-end framework, we also conducted the experiment on the AMI corpus. In NOISY, we used noisy speech data from the 1st channel in AMI as an input to the system. Note that BEAMFORMIT was worse than NOISY even with the enhanced signals. This phenomenon is sometimes observed in noisy speech recognition that the distortion caused by sole speech enhancement degrades the performance without re-training. Our end-to-end system jointly optimizes the speech enhancement part with the ASR objective, and can avoid such degradations.