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

By Ewan Dunbar, Julien Karadayi, Mathieu Bernard, Xuan-Nga Cao, Robin Algayres, Lucas Ondel, Laurent Besacier, Sakriani Watiasri Sakti, Emmanuel Dupoux. 2020

66 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Signal Processing

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Citations in the corpus

Speech Resynthesis from Discrete Disentangled Self-Supervised Representations (2021, neutral): “Recently, unsupervised learning of low bitrate speech representations was explored under the Zero-Resource Challenge [16, 17].”
Data Augmenting Contrastive Learning of Speech Representations in the Time Domain (2020, background): “The Zero Resource setting addresses the radical objective of building bricks of speech technology from raw audio, without any labels or text [1, 2].”
Towards Unsupervised Learning of Speech Features in the Wild (2021, background): “In the zero resource setting [26, 27], the aim is to build speech representations without any labels.”
Self-Supervised Speech Representation Learning: A Review (2022, methodology): “Such models are typically evaluated on cross-modal retrieval, although some work has also used the models for other downstream tasks such as the ZeroSpeech and SUPERB benchmark tasks [180].”
The Zero Resource Speech Benchmark 2021: Metrics and baselines for unsupervised spoken language modeling (2020, unknown): “Previous work (Versteegh et al., 2016; Dunbar et al., 2017, 2019, 2020) has focused on establishing benchmarks for unsupervised learning of an entire dialogue system, but has so far remained at a rather low level (acoustic, lexical).”
textless-lib: a Library for Textless Spoken Language Processing (2022, background): “There are plenty of ways to evaluate the overall pipeline (Lakhotia et al., 2021; Dunbar et al., 2019, 2020; Nguyen et al., 2020) and different ways to model the “pseudo-text” units (Shi et al., 2021; Kharitonov et al., 2021a; Polyak et al., 2021; Kreuk et al., 2021; Lee et al., 2021a), hence…”
Analyzing Speaker Information in Self-Supervised Models to Improve Zero-Resource Speech Processing (2021, background): “In contrast to continuous representation learning, acoustic unit discovery involves finding a set of discrete units corresponding to the phonetic inventory of a language [4, 15].”
The Zero Resource Speech Challenge 2021: Spoken Language Modelling (2021, unknown): “Previous work has shown that a low ABX score appears to be a very good predictor of good performance on TTS without T [30, 31, 32].”
A Brief Overview of Unsupervised Neural Speech Representation Learning (2022, background): “The longstanding zero resource speech challenge (ZeroSpeech) offers a new set of tasks for each edition (Versteegh et al. 2015; Dunbar et al. 2017, 2019, 2020, 2021) usually featuring a minimal-pair ABX task (Schatz et al. 2013, 2014).”