Abstract
Spectral mask estimation using bidirectional long short-term memory (BLSTM) neural networks has been widely used in various speech enhancement applications, and it has achieved great success when it is applied to multichannel enhancement techniques with a mask-based beamformer. However, when these masks are used for single channel speech enhancement they severely distort the speech signal and make them unsuitable for speech recognition. This paper proposes a student-teacher learning paradigm for single channel speech enhancement. The beamformed signal from multichannel enhancement is given as input to the teacher network to obtain soft masks. An additional cross-entropy loss term with the soft mask target is combined with the original loss, so that the student network with single-channel input is trained to mimic the soft mask obtained with multichannel input through beamforming. Experiments with the CHiME-4 challenge single channel track data shows improvement in ASR performance.
| Original language | English |
|---|---|
| Pages (from-to) | 3249-3253 |
| Number of pages | 5 |
| Journal | Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH |
| Volume | 2018-September |
| DOIs | |
| Publication status | Published - 2018 Jan 1 |
| Externally published | Yes |
| Event | 19th Annual Conference of the International Speech Communication, INTERSPEECH 2018 - Hyderabad, India Duration: 2018 Sep 2 → 2018 Sep 6 |
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Keywords
- BLSTM
- Mask estimation
- Speech enhancement
- Speech recognition
- Student-teacher learning
ASJC Scopus subject areas
- Language and Linguistics
- Human-Computer Interaction
- Signal Processing
- Software
- Modelling and Simulation
Cite this
Student-teacher learning for BLSTM mask-based speech enhancement. / Subramanian, Aswin Shanmugam; Chen, Szu Jui; Watanabe, Shinji.
In: Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH, Vol. 2018-September, 01.01.2018, p. 3249-3253.Research output: Contribution to journal › Conference article
}
TY - JOUR
T1 - Student-teacher learning for BLSTM mask-based speech enhancement
AU - Subramanian, Aswin Shanmugam
AU - Chen, Szu Jui
AU - Watanabe, Shinji
PY - 2018/1/1
Y1 - 2018/1/1
N2 - Spectral mask estimation using bidirectional long short-term memory (BLSTM) neural networks has been widely used in various speech enhancement applications, and it has achieved great success when it is applied to multichannel enhancement techniques with a mask-based beamformer. However, when these masks are used for single channel speech enhancement they severely distort the speech signal and make them unsuitable for speech recognition. This paper proposes a student-teacher learning paradigm for single channel speech enhancement. The beamformed signal from multichannel enhancement is given as input to the teacher network to obtain soft masks. An additional cross-entropy loss term with the soft mask target is combined with the original loss, so that the student network with single-channel input is trained to mimic the soft mask obtained with multichannel input through beamforming. Experiments with the CHiME-4 challenge single channel track data shows improvement in ASR performance.
AB - Spectral mask estimation using bidirectional long short-term memory (BLSTM) neural networks has been widely used in various speech enhancement applications, and it has achieved great success when it is applied to multichannel enhancement techniques with a mask-based beamformer. However, when these masks are used for single channel speech enhancement they severely distort the speech signal and make them unsuitable for speech recognition. This paper proposes a student-teacher learning paradigm for single channel speech enhancement. The beamformed signal from multichannel enhancement is given as input to the teacher network to obtain soft masks. An additional cross-entropy loss term with the soft mask target is combined with the original loss, so that the student network with single-channel input is trained to mimic the soft mask obtained with multichannel input through beamforming. Experiments with the CHiME-4 challenge single channel track data shows improvement in ASR performance.
KW - BLSTM
KW - Mask estimation
KW - Speech enhancement
KW - Speech recognition
KW - Student-teacher learning
UR - http://www.scopus.com/inward/record.url?scp=85054958811&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85054958811&partnerID=8YFLogxK
U2 - 10.21437/Interspeech.2018-2440
DO - 10.21437/Interspeech.2018-2440
M3 - Conference article
AN - SCOPUS:85054958811
VL - 2018-September
SP - 3249
EP - 3253
JO - Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH
JF - Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH
SN - 2308-457X
ER -