Enhanced Spectral Features for Distortion-Independent Acoustic Modeling

Peidong Wang, DeLiang Wang


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.


 DOI: 10.21437/Interspeech.2019-1493

Cite as: Wang, P., Wang, D. (2019) Enhanced Spectral Features for Distortion-Independent Acoustic Modeling. Proc. Interspeech 2019, 476-480, DOI: 10.21437/Interspeech.2019-1493.


@inproceedings{Wang2019,
  author={Peidong Wang and DeLiang Wang},
  title={{Enhanced Spectral Features for Distortion-Independent Acoustic Modeling}},
  year=2019,
  booktitle={Proc. Interspeech 2019},
  pages={476--480},
  doi={10.21437/Interspeech.2019-1493},
  url={http://dx.doi.org/10.21437/Interspeech.2019-1493}
}