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Cbldnn-based Speaker-independent Speech Separation Via Generative Adversarial Training
Li, Chenxing1,2; Zhu, Lei3; Xu, Shuang1; Gao, Peng3; Xu, Bo1
2018-04
Conference Name2018 IEEE International Conference on Acoustics, Speech and Signal Processing
Conference Date2020-4
Conference PlaceCalgary
Abstract

In this paper, we propose a speaker-independent multi-speaker monaural speech separation system (CBLDNN-GAT) based on convolutional, bidirectional long short-term memory, deep feedforward neural network (CBLDNN) with generative adversarial training (GAT). Our system aims at obtaining better speech quality instead of only minimizing a mean square error (MSE). In the initial phase, we utilize log-mel filterbank and pitch features to warm up our CBLDNN in a multi-task manner. Thus, the information that contributes to separating speech and improving speech quality is integrated into the model. We execute GAT throughout the training, which makes the separated speech indistinguishable from the real one. We evaluate CBLDNN-GAT on WSJ0-2mix dataset. The experimental results show that the proposed model achieves 11.0dB signal-to-distortion ratio (SDR) improvement, which is the new state-of-the-art result.

Language英语
Document Type会议论文
Identifierhttp://ir.ia.ac.cn/handle/173211/39853
Collection数字内容技术与服务研究中心_智能技术与系统工程
Corresponding AuthorLi, Chenxing
Affiliation1.Institute of Automation, Chinese Academy of Sciences
2.University of Chinese Academy of Sciences
3.AI Lab, Rokid Inc.
First Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Corresponding Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Recommended Citation
GB/T 7714
Li, Chenxing,Zhu, Lei,Xu, Shuang,et al. Cbldnn-based Speaker-independent Speech Separation Via Generative Adversarial Training[C],2018.
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