Enhanced Spectral Features for Distortion-Independent Acoustic Modeling

It has recently been shown that a distortion-independent acoustic modeling method is able to overcome the distortion problem caused by speech enhancement. In this study, we improve the distortion-independent acoustic model by feeding it with enhanced spectral features. Using enhanced magnitude spectra, the automatic speech recognition (ASR) system achieves a word error rate of 7.8% on the CHiME-2 corpus, outperforming our previous best system by more than 10% relatively. Compared with the corresponding enhanced waveform signal based system, systems using enhanced spectral features obtain up to 24% relative improvement. These comparisons show that speech enhancement is helpful for robust ASR and that enhanced spectral features are more suitable for ASR tasks than enhanced waveform signals.