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arXiv · 1811.02641

Building Corpora for Single-Channel Speech Separation Across Multiple Domains

Matthew Maciejewski·Gregory Sell·Leibny Paola Garcia-Perera·Shinji Watanabe·Sanjeev Khudanpur
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Abstract

To date, the bulk of research on single-channel speech separation has been conducted using clean, near-field, read speech, which is not representative of many modern applications. In this work, we develop a procedure for constructing high-quality synthetic overlap datasets, necessary for most deep learning-based separation frameworks. We produced datasets that are more representative of realistic applications using the CHiME-5 and Mixer 6 corpora and evaluate standard methods on this data to demonstrate the shortcomings of current source-separation performance. We also demonstrate the value of a wide variety of data in training robust models that generalize well to multiple conditions.

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Matthew Maciejewski, Gregory Sell, Leibny Paola Garcia-Perera, Shinji Watanabe, Sanjeev Khudanpur. 2018-11-06. Building Corpora for Single-Channel Speech Separation Across Multiple Domains. https://arxiv.org/abs/1811.02641

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