TE-KWS: Text-Informed Speech Enhancement for Noise-Robust Keyword Spotting-Reference-Cited by-同舟云学术

TE-KWS: Text-Informed Speech Enhancement for Noise-Robust Keyword Spotting

Published:2023-10-26 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 31st ACM International Conference on Multimedia
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Author:

Liu Dong¹^ORCID,Mao Qirong²^ORCID,Gao Lijian²^ORCID,Ren Qinghua²^ORCID,Chen Zhenghan³^ORCID,Dong Ming⁴^ORCID

Affiliation:

1. Jiangsu University & Shandong Youth University of Political Science, Zhenjiang, China

2. Jiangsu University, Zhenjiang, China

3. Peking University, Beijing, China

4. Wayne State University, Detroit, MI, USA

Funder

Post-graduate Research & Practice Innovation Program of Jiangsu Province

Key Project of National Nature Science Foundation of China

National Nature Science Foundation of China

Jiangsu key research and development plan

MTRAC Grant for Advanced Computing Technologies

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3581783.3612173

Reference72 articles.

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2. A curriculum learning method for improved noise robustness in automatic speech recognition

3. Deep Credible Metric Learning for Unsupervised Domain Adaptation Person Re-identification

4. Szu-Jui Chen , Aswin Shanmugam Subramanian , Hainan Xu, and Shinji Watanabe. 2018 . Building state-of-the-art distant speech recognition using the CHiME- 4 challenge with a setup of speech enhancement baseline. arXiv preprint arXiv:1803.10109 (2018). Szu-Jui Chen, Aswin Shanmugam Subramanian, Hainan Xu, and Shinji Watanabe. 2018. Building state-of-the-art distant speech recognition using the CHiME-4 challenge with a setup of speech enhancement baseline. arXiv preprint arXiv:1803.10109 (2018).

5. Deep attractor network for single-microphone speaker separation