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

arXiv: https://arxiv.org/abs/2010.05967 · HTML: https://mlsanity.com/paper/2010.05967/

## Key facts

- Cited by 39 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Oct 2020.

## Built on by (most cited first, 30 of 39)

- [Generative Spoken Language Modeling from Raw Audio](https://mlsanity.com/paper/2102.01192/) (2021)
- [Self-Supervised Speech Representation Learning: A Review](https://mlsanity.com/paper/2205.10643/) (2022)
- [Speech Resynthesis from Discrete Disentangled Self-Supervised Representations](https://mlsanity.com/paper/2104.00355/) (2021)
- [What Do Self-Supervised Speech Models Know About Words?](https://mlsanity.com/paper/2307.00162/) (2023)
- [The Zero Resource Speech Challenge 2021: Spoken language modelling](https://mlsanity.com/paper/2104.14700/) (2021)
- [Recent Advances in Discrete Speech Tokens: A Review](https://mlsanity.com/paper/2502.06490/) (2025)
- [A Large-Scale Evaluation of Speech Foundation Models](https://mlsanity.com/paper/2404.09385/) (2024)
- [Self-supervised language learning from raw audio: Lessons from the Zero Resource Speech Challenge](https://mlsanity.com/paper/2210.15759/) (2022)
- [textless-lib: a Library for Textless Spoken Language Processing](https://mlsanity.com/paper/2202.07359/) (2022)
- [SUPERB @ SLT 2022: Challenge on Generalization and Efficiency of Self-Supervised Speech Representation Learning](https://mlsanity.com/paper/2210.08634/) (2022)
- [Word Segmentation on Discovered Phone Units with Dynamic Programming and Self-Supervised Scoring](https://mlsanity.com/paper/2202.11929/) (2022)
- [DP-Parse: Finding Word Boundaries from Raw Speech with an Instance Lexicon](https://mlsanity.com/paper/2206.11332/) (2022)
- [A Masked Segmental Language Model for Unsupervised Natural Language Segmentation](https://mlsanity.com/paper/2104.07829/) (2021)
- [A Brief Overview of Unsupervised Neural Speech Representation Learning](https://mlsanity.com/paper/2203.01829/) (2022)
- [Double Articulation Analyzer with Prosody for Unsupervised Word and Phoneme Discovery](https://mlsanity.com/paper/2103.08199/) (2021)
- [Speech Sequence Embeddings using Nearest Neighbors Contrastive Learning](https://mlsanity.com/paper/2204.05148/) (2022)
- [Unsupervised Word Discovery: Boundary Detection with Clustering vs. Dynamic Programming](https://mlsanity.com/paper/2409.14486/) (2024)
- [Interpreting intermediate convolutional layers of generative CNNs trained on waveforms](https://mlsanity.com/paper/2104.09489/) (2021)
- [A phonetic model of non-native spoken word processing](https://mlsanity.com/paper/2101.11332/) (2021)
- [Unsupervised Automatic Speech Recognition: A Review](https://mlsanity.com/paper/2106.04897/) (2021)
- [Exploring How Generative Adversarial Networks Learn Phonological Representations](https://mlsanity.com/paper/2305.12501/) (2023)
- [Should Top-Down Clustering Affect Boundaries in Unsupervised Word Discovery?](https://mlsanity.com/paper/2507.19204/) (2025)
- [Discrete representations in neural models of spoken language](https://mlsanity.com/paper/2105.05582/) (2021)
- [VQ-T: RNN Transducers using Vector-Quantized Prediction Network States](https://mlsanity.com/paper/2208.01818/) (2022)
- [Revisiting speech segmentation and lexicon learning with better features](https://mlsanity.com/paper/2401.17902/) (2024)
- [Do Discrete Self-Supervised Representations of Speech Capture Tone Distinctions?](https://mlsanity.com/paper/2410.19935/) (2024)
- [Unsupervised Acoustic Unit Discovery by Leveraging a Language-Independent Subword Discriminative Feature Representation](https://mlsanity.com/paper/2104.00994/) (2021)
- [Unsupervised Word Segmentation from Discrete Speech Units in Low-Resource Settings](https://mlsanity.com/paper/2106.04298/) (2021)
- [Bootstrapping meaning through listening: Unsupervised learning of spoken sentence embeddings](https://mlsanity.com/paper/2210.12857/) (2022)
- [ZRIGF: An Innovative Multimodal Framework for Zero-Resource Image-Grounded Dialogue Generation](https://mlsanity.com/paper/2308.00400/) (2023)

## Abstract (arXiv)

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.