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| 作者 | Li GJ(李冠君)1,2 ; Liang S(梁山)1; Nie S(聂帅)1; Liu WJ(刘文举)1
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| 出版日期 | 2020
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| 会议日期 | 2020-10
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| 会议地点 | shanghai
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| 英文摘要 | The elastic spatial filter (ESF) proposed in recent years is a popular
multi-channel speech enhancement front end based on deep
neural network (DNN). It is suitable for real-time processing
and has shown promising automatic speech recognition (ASR)
results. However, the ESF only utilizes the knowledge of fixed
beamforming, resulting in limited noise reduction capabilities.
In this paper, we propose a DNN-based generalized sidelobe
canceller (GSC) that can automatically track the target speaker’s
direction in real time and use the blocking technique to generate
reference noise signals to further reduce noise from the
fixed beam pointing to the target direction. The coefficients in
the proposed GSC are fully learnable and an ASR criterion is
used to optimize the entire network. The 4-channel experiments
show that the proposed GSC achieves a relative word error rate
improvement of 27.0% compared to the raw observation, 20.6%
compared to the oracle direction-based traditional GSC, 10.5%
compared to the ESF and 7.9% compared to the oracle maskbased
generalized eigenvalue (GEV) beamformer. |
| 会议录出版者 | Interspeech
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| 会议录出版地 | shanhai
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| 源URL | [http://ir.ia.ac.cn/handle/173211/44939]  |
| 专题 | 模式识别国家重点实验室_智能交互
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| 通讯作者 | Li GJ(李冠君) |
| 作者单位 | 1.Institute of Automation, Chinese Academy of Sciences
2.School of Artificial Intelligence, University of Chinese Academy of Sciences, China
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推荐引用方式
GB/T 7714 |
Li GJ,Liang S,Nie S,et al. Deep Neural Network-Based Generalized Sidelobe Canceller for Robust Multi-channel Speech Recognition[C]. 见:. shanghai. 2020-10.
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