LCCCRN: robust deep learning-based speech enhancement

Chun-Yin Yeung; Steve W. Y. Mung; Yat Sze Choy; Daniel P. K. Lun

doi:10.1117/12.2626108

30 April 2022 LCCCRN: robust deep learning-based speech enhancement

Chun-Yin Yeung, Steve W. Y. Mung, Yat Sze Choy, Daniel P. K. Lun

Proceedings Volume 12177, International Workshop on Advanced Imaging Technology (IWAIT) 2022; 121772N (2022) https://doi.org/10.1117/12.2626108
Event: International Workshop on Advanced Imaging Technology 2022 (IWAIT 2022), 2022, Hong Kong, China

Abstract

Deep learning-based speech enhancement methods make use of their nonlinearity properties to estimate the speech and noise signals, especially the nonstationary noise. DCCRN, in particular, achieves state-of-the-art performance on speech intelligibility. However, the nonlinear property also causes concern about the robustness of the method. Novel and unexpected noises can be generated if the noisy input speech is beyond the operation condition of the method. In this paper, we propose a hybrid framework called LDCCRN, which integrates a traditional speech enhancement method LogMMSE-EM and DCCRN. The proposed framework leverages the strength of both approaches to improve the robustness in speech enhancement. While the DCCRN continues to remove the nonstationary noise in the speech, the novel noises generated by DCCRN, if any, are effectively suppressed by LogMMSE-EM. As shown in our experimental results, the proposed method achieves better performance over the traditional approaches measured with standard evaluation methods.

Citation Download Citation

Chun-Yin Yeung, Steve W. Y. Mung, Yat Sze Choy, and Daniel P. K. Lun "LCCCRN: robust deep learning-based speech enhancement", Proc. SPIE 12177, International Workshop on Advanced Imaging Technology (IWAIT) 2022, 121772N (30 April 2022); https://doi.org/10.1117/12.2626108

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