16th Annual Conference of the International Speech Communication Association

Dresden, Germany
September 6-10, 2015

Bilinear Map of Filter-Bank Outputs for DNN-Based Speech Recognition

Tetsuji Ogawa (1), Kenshiro Ueda (1), Kouichi Katsurada (2), Tetsunori Kobayashi (1), Tsuneo Nitta (1)

(1) Waseda University, Japan
(2) Toyohashi University of Technology, Japan

Filter-bank outputs are extended into tensors to yield precise acoustic features for speech recognition using deep neural networks (DNNs). The filter-bank outputs with temporal contexts form a time-frequency pattern of speech and have been shown to be effective as a feature parameter for DNN-based acoustic models. We attempt to project the filter-bank outputs onto a tensor product space using decorrelation followed by a bilinear map to improve acoustic separability in feature extraction. This extension makes extracting a more precise structure of the time-frequency pattern possible because the bilinear map yields higher-order correlations of features. Experimental comparisons carried out in phoneme recognition demonstrate that the tensor feature provides comparable results to the filter-bank feature, and the fusion of the two features yields an improvement over each feature.

Full Paper

Bibliographic reference.  Ogawa, Tetsuji / Ueda, Kenshiro / Katsurada, Kouichi / Kobayashi, Tetsunori / Nitta, Tsuneo (2015): "Bilinear map of filter-bank outputs for DNN-based speech recognition", In INTERSPEECH-2015, 16-20.