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The Zero Resource Speech Challenge 2021: Spoken language modelling

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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https://hal.inria.fr/hal-03329301
Contributor : Emmanuel Dupoux Connect in order to contact the contributor
Submitted on : Monday, October 11, 2021 - 3:39:36 PM
Last modification on : Friday, November 18, 2022 - 9:24:55 AM

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Ewan Dunbar, Mathieu Bernard, Nicolas Hamilakis, Tu Anh Nguyen, Maureen de Seyssel, et al.. The Zero Resource Speech Challenge 2021: Spoken language modelling. Interspeech 2021 - Conference of the International Speech Communication Association, Aug 2021, Brno, Czech Republic. ⟨10.1109/TPAMI.2021.3083839⟩. ⟨hal-03329301v2⟩

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