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arXiv · 2104.14700

The Zero Resource Speech Challenge 2021: Spoken language modelling

Ewan Dunbar·Mathieu Bernard·Nicolas Hamilakis·Tu Anh Nguyen·Maureen de Seyssel·Patricia Rozé·Morgane Rivière·Eugene Kharitonov·Emmanuel Dupoux
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Abstract

We present the Zero Resource Speech Challenge 2021, which asks participants to learn a language model directly from audio, without any text or labels. The challenge is based on the Libri-light dataset, which provides up to 60k hours of audio from English audio books without any associated text. We provide a pipeline baseline system consisting on an encoder based on contrastive predictive coding (CPC), a quantizer ($k$-means) and a standard language model (BERT or LSTM). The metrics evaluate the learned representations at the acoustic (ABX discrimination), lexical (spot-the-word), syntactic (acceptability judgment) and semantic levels (similarity judgment). We present an overview of the eight submitted systems from four groups and discuss the main results.

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Ewan Dunbar, Mathieu Bernard, Nicolas Hamilakis, Tu Anh Nguyen, Maureen de Seyssel, Patricia Rozé, Morgane Rivière, Eugene Kharitonov, Emmanuel Dupoux. 2021-04-29. The Zero Resource Speech Challenge 2021: Spoken language modelling. https://arxiv.org/abs/2104.14700

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