Abstract
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.
| Original language | English (US) |
|---|---|
| Pages | 2066-2069 |
| Number of pages | 4 |
| State | Published - Dec 1 2010 |
| Event | 11th Annual Conference of the International Speech Communication Association: Spoken Language Processing for All, INTERSPEECH 2010 - Makuhari, Chiba, Japan Duration: Sep 26 2010 → Sep 30 2010 |
Other
| Other | 11th Annual Conference of the International Speech Communication Association: Spoken Language Processing for All, INTERSPEECH 2010 |
|---|---|
| Country | Japan |
| City | Makuhari, Chiba |
| Period | 9/26/10 → 9/30/10 |
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Keywords
- Ideal binary mask classification
- Missing feature
- Robust speech recognition
ASJC Scopus subject areas
- Language and Linguistics
- Speech and Hearing
Cite this
Robust automatic speech recognition with decoder oriented ideal binary mask estimation. / Kim, Lae Hoon; Kim, Kyung Tae; Hasegawa-Johnson, Mark Allan.
2010. 2066-2069 Paper presented at 11th Annual Conference of the International Speech Communication Association: Spoken Language Processing for All, INTERSPEECH 2010, Makuhari, Chiba, Japan.Research output: Contribution to conference › Paper
}
TY - CONF
T1 - Robust automatic speech recognition with decoder oriented ideal binary mask estimation
AU - Kim, Lae Hoon
AU - Kim, Kyung Tae
AU - Hasegawa-Johnson, Mark Allan
PY - 2010/12/1
Y1 - 2010/12/1
N2 - 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.
AB - 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.
KW - Ideal binary mask classification
KW - Missing feature
KW - Robust speech recognition
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M3 - Paper
AN - SCOPUS:79959819577
SP - 2066
EP - 2069
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