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arXiv · 1912.08462

End-to-end training of time domain audio separation and recognition

Thilo von Neumann·Keisuke Kinoshita·Lukas Drude·Christoph Boeddeker·Marc Delcroix·Tomohiro Nakatani·Reinhold Haeb-Umbach
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Abstract

The rising interest in single-channel multi-speaker speech separation sparked development of End-to-End (E2E) approaches to multi-speaker speech recognition. However, up until now, state-of-the-art neural network-based time domain source separation has not yet been combined with E2E speech recognition. We here demonstrate how to combine a separation module based on a Convolutional Time domain Audio Separation Network (Conv-TasNet) with an E2E speech recognizer and how to train such a model jointly by distributing it over multiple GPUs or by approximating truncated back-propagation for the convolutional front-end. To put this work into perspective and illustrate the complexity of the design space, we provide a compact overview of single-channel multi-speaker recognition systems. Our experiments show a word error rate of 11.0% on WSJ0-2mix and indicate that our joint time domain model can yield substantial improvements over cascade DNN-HMM and monolithic E2E frequency domain systems proposed so far.

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Thilo von Neumann, Keisuke Kinoshita, Lukas Drude, Christoph Boeddeker, Marc Delcroix, Tomohiro Nakatani, Reinhold Haeb-Umbach. 2020-04-13. End-to-end training of time domain audio separation and recognition. https://doi.org/10.1109/icassp40776.2020.9053461

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