TY  - CONF
A1  - Cheng, Jiaming
A1  - Liang, Ruiyu
A1  - Xie, Yue
A1  - Zhao, Li
A1  - Schuller, Björn
A1  - Jia, Jie
A1  - Peng, Yiyuan
A2  - Ko, Hanseok
A2  - Hansen, John H. L.
T1  - Cross-layer similarity knowledge distillation for speech enhancement
T2  - Interspeech 2022, Incheon, Korea, 18-22 September 2022
N2  - Speech enhancement (SE) algorithms based on deep neural networks (DNNs) often encounter challenges of limited hardware resources or strict latency requirements when deployed in real-world scenarios. However, a strong enhancement effect typically requires a large DNN. In this paper, a knowledge distillation framework for SE is proposed to compress the DNN model. We study the strategy of cross-layer connection paths, which fuses multi-level information from the teacher and transfers it to the student. To adapt to the SE task, we propose a frame-level similarity distillation loss. We apply this method to the deep complex convolution recurrent network (DCCRN) and make targeted adjustments. Experimental results show that the proposed method considerably improves the enhancement effect of the compressed DNN and outperforms other distillation methods.
Y1  - 2022
UR  - https://opus.bibliothek.uni-augsburg.de/opus4/frontdoor/index/index/docId/99291
UR  - https://nbn-resolving.org/urn:nbn:de:bvb:384-opus4-992916
SP  - 926
EP  - 930
PB  - ISCA
CY  - Baixas
ER  -