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.
@article{arxiv.2104.14700,
title = {The Zero Resource Speech Challenge 2021: Spoken language modelling},
author = {Ewan Dunbar and Mathieu Bernard and Nicolas Hamilakis and Tu Anh Nguyen and Maureen de Seyssel and Patricia Rozé and Morgane Rivière and Eugene Kharitonov and Emmanuel Dupoux},
journal= {arXiv preprint arXiv:2104.14700},
year = {2021}
}
Comments
Submitted to Interspeech 2021. arXiv admin note: text overlap with arXiv:2011.11588