Abstract
We present noise robust automatic speech recognition (ASR) using sparseness-based underdetermined blind source separation (BSS) technique. As a representative underdetermined BSS method, we utilized time-frequency masking in this paper. Although time-frequency masking is able to separate target speech from interferences effectively, one should consider two problems. One is that masking does not work well in noisy or reverberant environment. Another is that masking itself might cause some distortion of the target speech. For the former, we apply our time-frequency masking method [7] which can separate the target signal robustly even in noisy and reverberant environment. Next, investigating the distortion caused by time-frequency masking, we reveal following facts through experiments: 1) soft mask is better than binary mask in terms of recognition performance and 2) cepstral mean normalization (CMN) reduces the distortion, especially for that caused by soft mask. At the end, we evaluate the recognition performance of our method in noisy and reverberant real environment.
| Original language | English |
|---|---|
| Pages (from-to) | 1955-1958 |
| Number of pages | 4 |
| Journal | Unknown Journal |
| Publication status | Published - 2009 |
| Externally published | Yes |
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Keywords
- Blind source separation
- Robust ASR
- Speech sparseness
- Stereo-input
- Time-frequency mask
ASJC Scopus subject areas
- Human-Computer Interaction
- Signal Processing
- Software
- Sensory Systems
Cite this
Stereo-input speech recognition using sparseness-based time-frequency masking in a reverberant environment. / Izumi, Yosuke; Nishiki, Kenta; Watanabe, Shinji; Nishimoto, Takuya; Ono, Nobutaka; Sagayama, Shigeki.
In: Unknown Journal, 2009, p. 1955-1958.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Stereo-input speech recognition using sparseness-based time-frequency masking in a reverberant environment
AU - Izumi, Yosuke
AU - Nishiki, Kenta
AU - Watanabe, Shinji
AU - Nishimoto, Takuya
AU - Ono, Nobutaka
AU - Sagayama, Shigeki
PY - 2009
Y1 - 2009
N2 - We present noise robust automatic speech recognition (ASR) using sparseness-based underdetermined blind source separation (BSS) technique. As a representative underdetermined BSS method, we utilized time-frequency masking in this paper. Although time-frequency masking is able to separate target speech from interferences effectively, one should consider two problems. One is that masking does not work well in noisy or reverberant environment. Another is that masking itself might cause some distortion of the target speech. For the former, we apply our time-frequency masking method [7] which can separate the target signal robustly even in noisy and reverberant environment. Next, investigating the distortion caused by time-frequency masking, we reveal following facts through experiments: 1) soft mask is better than binary mask in terms of recognition performance and 2) cepstral mean normalization (CMN) reduces the distortion, especially for that caused by soft mask. At the end, we evaluate the recognition performance of our method in noisy and reverberant real environment.
AB - We present noise robust automatic speech recognition (ASR) using sparseness-based underdetermined blind source separation (BSS) technique. As a representative underdetermined BSS method, we utilized time-frequency masking in this paper. Although time-frequency masking is able to separate target speech from interferences effectively, one should consider two problems. One is that masking does not work well in noisy or reverberant environment. Another is that masking itself might cause some distortion of the target speech. For the former, we apply our time-frequency masking method [7] which can separate the target signal robustly even in noisy and reverberant environment. Next, investigating the distortion caused by time-frequency masking, we reveal following facts through experiments: 1) soft mask is better than binary mask in terms of recognition performance and 2) cepstral mean normalization (CMN) reduces the distortion, especially for that caused by soft mask. At the end, we evaluate the recognition performance of our method in noisy and reverberant real environment.
KW - Blind source separation
KW - Robust ASR
KW - Speech sparseness
KW - Stereo-input
KW - Time-frequency mask
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UR - http://www.scopus.com/inward/citedby.url?scp=70450163599&partnerID=8YFLogxK
M3 - Article
SP - 1955
EP - 1958
JO - Nuclear Physics A
JF - Nuclear Physics A
SN - 0375-9474
ER -