Related papers: The Zero Resource Speech Challenge 2020: Discovering discrete subword and word units

The Zero Resource Speech Challenge 2020: Discovering discrete subword and word units

URL: http://arxiv.org/abs/2010.05967v1
Date: Mon, 12 Oct 2020 18:56:48 GMT
Title: The Zero Resource Speech Challenge 2020: Discovering discrete subword and word units
Authors: Ewan Dunbar and Julien Karadayi and Mathieu Bernard and Xuan-Nga Cao and Robin Algayres and Lucas Ondel and Laurent Besacier and Sakriani Sakti and Emmanuel Dupoux
Abstract summary: Zero Resource Speech Challenge 2020 aims at learning speech representations from raw audio signals without any labels. We present the results of the twenty submitted models and discuss the implications of the main findings for unsupervised speech learning.
Score: 40.41406551797358
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: We present the Zero Resource Speech Challenge 2020, which aims at learning speech representations from raw audio signals without any labels. It combines the data sets and metrics from two previous benchmarks (2017 and 2019) and features two tasks which tap into two levels of speech representation. The first task is to discover low bit-rate subword representations that optimize the quality of speech synthesis; the second one is to discover word-like units from unsegmented raw speech. We present the results of the twenty submitted models and discuss the implications of the main findings for unsupervised speech learning.

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