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Self-supervised language learning from raw audio: Lessons from the Zero Resource Speech Challenge

IEEE journal of selected topics in signal processing, 2022-10 [Peer Reviewed Journal]

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Title:
Self-supervised language learning from raw audio: Lessons from the Zero Resource Speech Challenge
Author: Dunbar, Ewan ; Hamilakis, Nicolas ; Dupoux, Emmanuel
Subjects: Artificial Intelligence ; Cognitive science ; Computation and Language ; Computer Science ; Linguistics
Is Part Of: IEEE journal of selected topics in signal processing, 2022-10
Description: Recent progress in self-supervised or unsupervised machine learning has opened the possibility of building a full speech processing system from raw audio without using any textual representations or expert labels such as phonemes, dictionaries or parse trees. The contribution of the Zero Resource Speech Challenge series since 2015 has been to break down this long-term objective into four well-defined tasks-Acoustic Unit Discovery, Spoken Term Discovery, Discrete Resynthesis, and Spoken Language Modeling-and introduce associated metrics and benchmarks enabling model comparison and cumulative progress. We present an overview of the six editions of this challenge series since 2015, discuss the lessons learned, and outline the areas which need more work or give puzzling results.
Publisher: IEEE
Language: English
Identifier: ISSN: 1932-4553
EISSN: 1941-0484
DOI: 10.1109/jstsp.2022.3206084
Source: Hyper Article en Ligne (HAL) (Open Access)

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