Handling Background Noise in Neural Speech Generation

Denton, Tom; Luebs, Alejandro; Chinen, Michael; Lim, Felicia SC; Storus, Andrew; Yeh, Hengchin; Kleijn, Willem; Skoglund, Jan

doi:10.25455/wgtn.14865042.v1

Handling Background Noise in Neural Speech Generation.pdf (217.06 kB)

Handling Background Noise in Neural Speech Generation

conference contribution

posted on 2021-06-28, 07:41 authored by Tom Denton, Alejandro Luebs, Michael Chinen, Felicia SC Lim, Andrew Storus, Hengchin Yeh, Willem KleijnWillem Kleijn, Jan Skoglund

Recent advances in neural-network based generative modeling of speech has shown great potential for speech coding. However, the performance of such models drops when the input is not clean speech, e.g., in the presence of background noise, preventing its use in practical applications. In this paper we examine the reason and discuss methods to overcome this issue. Placing a denoising preprocessing stage when extracting features and target clean speech during training is shown to be the best performing strategy.

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Preferred citation

Denton, T., Luebs, A., Chinen, M., Lim, F. S. C., Storus, A., Yeh, H., Kleijn, W. B. & Skoglund, J. (2020, November). Handling Background Noise in Neural Speech Generation. In 2020 54th Asilomar Conference on Signals, Systems, and Computers 2020 54th Asilomar Conference on Signals, Systems, and Computers (00 pp. 667-671). IEEE. https://doi.org/10.1109/ieeeconf51394.2020.9443535