X-tasnet: Robust And Accurate Time-domain Speaker Extraction Network

Abstract

Extracting the speech of a target speaker from mixed audios, based on a reference speech from the target speaker, is a challenging yet powerful technology in speech processing. Recent studies of speaker-independent speech separation, such as TasNet, have shown promising results by applying deep neural networks over the time-domain waveform. Such separation neural network does not directly generate reliable and accurate output when target speakers are specified, because of the necessary prior on the number of speakers and the lack of robustness when dealing with audios with absent speakers. In this paper, we break these limitations by introducing a new speaker-aware speech masking method, called X-TaSNet. Our proposal adopts new strategies, including a distortion-based loss and corresponding alternating training scheme, to better address the robustness issue. X-TaSNet significantly enhances the extracted speech quality, doubling SDRi and SI-SNRi of the output speech audio over state-of-

Stats

• citations24
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• github stars0
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• heat score10.48
• arxiv keyzhang2020x

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