INTERSPEECH 2014
15th Annual Conference of the International Speech Communication Association

Singapore
September 14-18, 2014

A Long, Deep and Wide Artificial Neural Net for Robust Speech Recognition in Unknown Noise

Feipeng Li, Phani S. Nidadavolu, Hynek Hermansky

Johns Hopkins University, USA

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.

Full Paper

Bibliographic reference.  Li, Feipeng / Nidadavolu, Phani S. / Hermansky, Hynek (2014): "A long, deep and wide artificial neural net for robust speech recognition in unknown noise", In INTERSPEECH-2014, 358-362.