11th Annual Conference of the International Speech Communication Association

Makuhari, Chiba, Japan
September 26-30. 2010

Robust Automatic Speech Recognition with Decoder Oriented Ideal Binary Mask Estimation

Lae-Hoon Kim, Kyung-Tae Kim, Mark Hasegawa-Johnson

University of Illinois at Urbana-Champaign, USA

In this paper, we propose a joint optimal method for automatic speech recognition (ASR) and ideal binary mask (IBM) estimation in transformed into the cepstral domain through a newly derived generalized expectation maximization algorithm. First, cepstral domain missing feature marginalization is established using a linear transformation, after tying the mean and variance of non-existing cepstral coefficients. Second, IBM estimation is formulated using a generalized expectation maximization algorithm directly to optimize the ASR performance. Experimental results show that even in highly non-stationary mismatch condition (dance music as background noise), the proposed method achieves much higher absolute ASR accuracy improvement ranging from 14.69% at 0 dB SNR to 40.10% at 15 dB SNR compared with the conventional noise suppression method.

Full Paper

Bibliographic reference.  Kim, Lae-Hoon / Kim, Kyung-Tae / Hasegawa-Johnson, Mark (2010): "Robust automatic speech recognition with decoder oriented ideal binary mask estimation", In INTERSPEECH-2010, 2066-2069.