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arXiv · 2011.15003

Convolutive Transfer Function Invariant SDR training criteria for Multi-Channel Reverberant Speech Separation

Christoph Boeddeker·Wangyou Zhang·Tomohiro Nakatani·Keisuke Kinoshita·Tsubasa Ochiai·Marc Delcroix·Naoyuki Kamo·Yanmin Qian·Reinhold Haeb-Umbach
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Abstract

Time-domain training criteria have proven to be very effective for the separation of single-channel non-reverberant speech mixtures. Likewise, mask-based beamforming has shown impressive performance in multi-channel reverberant speech enhancement and source separation. Here, we propose to combine neural network supported multi-channel source separation with a time-domain training objective function. For the objective we propose to use a convolutive transfer function invariant Signal-to-Distortion Ratio (CI-SDR) based loss. While this is a well-known evaluation metric (BSS Eval), it has not been used as a training objective before. To show the effectiveness, we demonstrate the performance on LibriSpeech based reverberant mixtures. On this task, the proposed system approaches the error rate obtained on single-source non-reverberant input, i.e., LibriSpeech test_clean, with a difference of only 1.2 percentage points, thus outperforming a conventional permutation invariant training based system and alternative objectives like Scale Invariant Signal-to-Distortion Ratio by a large margin.

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Christoph Boeddeker, Wangyou Zhang, Tomohiro Nakatani, Keisuke Kinoshita, Tsubasa Ochiai, Marc Delcroix, Naoyuki Kamo, Yanmin Qian, Reinhold Haeb-Umbach. 2020-11-30. Convolutive Transfer Function Invariant SDR training criteria for Multi-Channel Reverberant Speech Separation. https://arxiv.org/abs/2011.15003

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