ISCA Archive Interspeech 2014
ISCA Archive Interspeech 2014

A long, deep and wide artificial neural net for robust speech recognition in unknown noise

Feipeng Li, Phani S. Nidadavolu, Hynek Hermansky

A long deep and wide artificial neural net (LDWNN) with multiple ensemble neural nets for individual frequency subbands is proposed for robust speech recognition in unknown noise. It is assumed that the effect of arbitrary additive noise on speech recognition can be approximated by white noise (or speech-shaped noise) of similar level across multiple frequency subbands. The ensemble neural nets are trained in clean and speech-shaped noise at 20, 10, and 5 dB SNR to accommodate noise of different levels, followed by a neural net trained to select the most suitable neural net for optimum information extraction within a frequency subband. The posteriors from multiple frequency subbands are fused by another neural net to give a more reliable estimation. Experimental results show that the subband ensemble net adapts well to unknown noise.


doi: 10.21437/Interspeech.2014-84

Cite as: Li, F., Nidadavolu, P.S., Hermansky, H. (2014) A long, deep and wide artificial neural net for robust speech recognition in unknown noise. Proc. Interspeech 2014, 358-362, doi: 10.21437/Interspeech.2014-84

@inproceedings{li14b_interspeech,
  author={Feipeng Li and Phani S. Nidadavolu and Hynek Hermansky},
  title={{A long, deep and wide artificial neural net for robust speech recognition in unknown noise}},
  year=2014,
  booktitle={Proc. Interspeech 2014},
  pages={358--362},
  doi={10.21437/Interspeech.2014-84},
  issn={2308-457X}
}