# The Zero Resource Speech Challenge 2019: TTS without T

arXiv: https://arxiv.org/abs/1904.11469 · HTML: https://mlsanity.com/paper/1904.11469/

## Key facts

- Cited by 38 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Apr 2019.

## Built on by (most cited first, 30 of 38)

- [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)
- [Recent Advances in Speech Language Models: A Survey](https://mlsanity.com/paper/2410.03751/) (2024)
- [SQ-VAE: Variational Bayes on Discrete Representation with Self-annealed Stochastic Quantization](https://mlsanity.com/paper/2205.07547/) (2022)
- [The Zero Resource Speech Challenge 2021: Spoken language modelling](https://mlsanity.com/paper/2104.14700/) (2021)
- [Self-supervised language learning from raw audio: Lessons from the Zero Resource Speech Challenge](https://mlsanity.com/paper/2210.15759/) (2022)
- [Visually grounded models of spoken language: A survey of datasets, architectures and evaluation techniques](https://mlsanity.com/paper/2104.13225/) (2021)
- [Segmental Contrastive Predictive Coding for Unsupervised Word Segmentation](https://mlsanity.com/paper/2106.02170/) (2021)
- [textless-lib: a Library for Textless Spoken Language Processing](https://mlsanity.com/paper/2202.07359/) (2022)
- [Word Segmentation on Discovered Phone Units with Dynamic Programming and Self-Supervised Scoring](https://mlsanity.com/paper/2202.11929/) (2022)
- [Acoustic word embeddings for zero-resource languages using self-supervised contrastive learning and multilingual adaptation](https://mlsanity.com/paper/2103.10731/) (2021)
- [Generative Spoken Language Model based on continuous word-sized audio tokens](https://mlsanity.com/paper/2310.05224/) (2023)
- [DUB: Discrete Unit Back-translation for Speech Translation](https://mlsanity.com/paper/2305.11411/) (2023)
- [KazakhTTS2: Extending the Open-Source Kazakh TTS Corpus With More Data, Speakers, and Topics](https://mlsanity.com/paper/2201.05771/) (2022)
- [A Brief Overview of Unsupervised Neural Speech Representation Learning](https://mlsanity.com/paper/2203.01829/) (2022)
- [fastabx: A library for efficient computation of ABX discriminability](https://mlsanity.com/paper/2505.02692/) (2025)
- [Low-Resource Domain Adaptation for Speech LLMs via Text-Only Fine-Tuning](https://mlsanity.com/paper/2506.05671/) (2025)
- [ZR-2021VG: Zero-Resource Speech Challenge, Visually-Grounded Language Modelling track, 2021 edition](https://mlsanity.com/paper/2107.06546/) (2021)
- [Protecting gender and identity with disentangled speech representations](https://mlsanity.com/paper/2104.11051/) (2021)
- [Unsupervised Automatic Speech Recognition: A Review](https://mlsanity.com/paper/2106.04897/) (2021)
- [Self-supervised speech unit discovery from articulatory and acoustic features using VQ-VAE](https://mlsanity.com/paper/2206.08790/) (2022)
- [An Empirical Recipe for Universal Phone Recognition](https://mlsanity.com/paper/2603.29042/) (2026)
- [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)
- [Repeat after me: Self-supervised learning of acoustic-to-articulatory mapping by vocal imitation](https://mlsanity.com/paper/2204.02269/) (2022)
- [A multilingual training strategy for low resource Text to Speech](https://mlsanity.com/paper/2409.01217/) (2024)
- [Breaking the Transcription Bottleneck: Fine-tuning ASR Models for Extremely Low-Resource Fieldwork Languages](https://mlsanity.com/paper/2506.17459/) (2025)
- [Unsupervised Acoustic Unit Discovery by Leveraging a Language-Independent Subword Discriminative Feature Representation](https://mlsanity.com/paper/2104.00994/) (2021)

## Abstract (arXiv)

We present the Zero Resource Speech Challenge 2019, which proposes to build a speech synthesizer without any text or phonetic labels: hence, TTS without T (text-to-speech without text). We provide raw audio for a target voice in an unknown language (the Voice dataset), but no alignment, text or labels. Participants must discover subword units in an unsupervised way (using the Unit Discovery dataset) and align them to the voice recordings in a way that works best for the purpose of synthesizing novel utterances from novel speakers, similar to the target speaker's voice. We describe the metrics used for evaluation, a baseline system consisting of unsupervised subword unit discovery plus a standard TTS system, and a topline TTS using gold phoneme transcriptions. We present an overview of the 19 submitted systems from 10 teams and discuss the main results.