Robust automatic speech recognition with decoder oriented ideal binary mask estimation

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.

• Type: Journal
• Year: 2010
• Where: INTERSPEECH
• Authors: Lae-Hoon Kim, Kyung-Tae Kim, Mark Hasegawa-Johnson
• Keywords: Cepstral Domain, Expectation Maximization Algorithm, Generalized Expectation Maximization, INTERSPEECH 2010, Signal Processing