TEA-PSE 3.0: TENCENT-ETHEREAL-AUDIO-LAB PERSONALIZED SPEECH ENHANCEMENT SYSTEM FOR ICASSP 2023 DNS-CHALLENGE
Yukai Jv (Shaanxi Provincial Key Laboratory of Speech and Image Information Processing, School of Computer Science, Northwestern Polytechnical University); Jun Chen (Tencent); Shimin Zhang (Northwestern Polytechnical University); Shulin He (College of Computer Science, Inner Mongolia University); Wei Rao (Tencent); weixin zhu (tencent); Yannan Wang (Tencent); Tao Yu (Tencent); Shi-dong Shang (tencent)
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10 Jun 2023
This paper introduces the Unbeatable Team's submission to the ICASSP 2023 Deep Noise Suppression (DNS) Challenge. We expand our previous work, TEA-PSE, to its upgraded version -- TEA-PSE 3.0. Specifically, TEA-PSE 3.0 incorporates a residual LSTM after squeezed temporal convolution network (S-TCN) to enhance sequence modeling capabilities. Additionally, the local-global representation (LGR) structure is introduced to boost speaker information extraction, and multi-STFT resolution loss is used to effectively capture the time-frequency characteristics of the speech signals. Moreover, retraining methods are employed based on the freeze training strategy to fine-tune the system. According to the official results, TEA-PSE 3.0 ranks 1st in both ICASSP 2023 DNS-Challenge track 1 and track 2.
