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arXiv · 2309.17384

Toward Universal Speech Enhancement for Diverse Input Conditions

Wangyou Zhang·Kohei Saijo·Zhong-Qiu Wang·Shinji Watanabe·Yanmin Qian
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Abstract

The past decade has witnessed substantial growth of data-driven speech enhancement (SE) techniques thanks to deep learning. While existing approaches have shown impressive performance in some common datasets, most of them are designed only for a single condition (e.g., single-channel, multi-channel, or a fixed sampling frequency) or only consider a single task (e.g., denoising or dereverberation). Currently, there is no universal SE approach that can effectively handle diverse input conditions with a single model. In this paper, we make the first attempt to investigate this line of research. First, we devise a single SE model that is independent of microphone channels, signal lengths, and sampling frequencies. Second, we design a universal SE benchmark by combining existing public corpora with multiple conditions. Our experiments on a wide range of datasets show that the proposed single model can successfully handle diverse conditions with strong performance.

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Wangyou Zhang, Kohei Saijo, Zhong-Qiu Wang, Shinji Watanabe, Yanmin Qian. 2023-09-29. Toward Universal Speech Enhancement for Diverse Input Conditions. https://arxiv.org/abs/2309.17384

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