# CML-TTS A Multilingual Dataset for Speech Synthesis in Low-Resource Languages

arXiv: https://arxiv.org/abs/2306.10097 · HTML: https://mlsanity.com/paper/2306.10097/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Jun 2023.

## Built on by (most cited first, 15 of 15)

- [XTTS: a Massively Multilingual Zero-Shot Text-to-Speech Model](https://mlsanity.com/paper/2406.04904/) (2024)
- [Koel-TTS: Enhancing LLM based Speech Generation with Preference Alignment and Classifier Free Guidance](https://mlsanity.com/paper/2502.05236/) (2025)
- [Streaming Sequence-to-Sequence Learning with Delayed Streams Modeling](https://mlsanity.com/paper/2509.08753/) (2025)
- [OmniVoice: Towards Omnilingual Zero-Shot Text-to-Speech with Diffusion Language Models](https://mlsanity.com/paper/2604.00688/) (2026)
- [FLEURS-R: A Restored Multilingual Speech Corpus for Generation Tasks](https://mlsanity.com/paper/2408.06227/) (2024)
- [RFWave: Multi-band Rectified Flow for Audio Waveform Reconstruction](https://mlsanity.com/paper/2403.05010/) (2024)
- [Simultaneous Speech-to-Speech Translation Without Aligned Data](https://mlsanity.com/paper/2602.11072/) (2026)
- [A multilingual training strategy for low resource Text to Speech](https://mlsanity.com/paper/2409.01217/) (2024)
- [Synthetic Voice Data for Automatic Speech Recognition in African Languages](https://mlsanity.com/paper/2507.17578/) (2025)
- [Align2Speak: Improving TTS for Low Resource Languages via ASR-Guided Online Preference Optimization](https://mlsanity.com/paper/2509.21718/) (2025)
- [The Impact of Prosodic Segmentation on Speech Synthesis of Spontaneous Speech](https://mlsanity.com/paper/2511.14779/) (2025)
- [Tagarela - A Portuguese speech dataset from podcasts](https://mlsanity.com/paper/2603.15326/) (2026)
- [Saar-Voice: A Multi-Speaker Saarbr"ucken Dialect Speech Corpus](https://mlsanity.com/paper/2604.11803/) (2026)
- [Extracting accent features in spoken Brazilian Portuguese without sociolinguistic labels](https://mlsanity.com/paper/2605.30457/) (2026)
- [Data Scale, Not Latency, Shapes Cross-Lingual Encoder Transfer in Streaming ASR](https://mlsanity.com/paper/2606.24169/) (2026)

## Abstract (arXiv)

In this paper, we present CML-TTS, a recursive acronym for CML-Multi-Lingual-TTS, a new Text-to-Speech (TTS) dataset developed at the Center of Excellence in Artificial Intelligence (CEIA) of the Federal University of Goias (UFG). CML-TTS is based on Multilingual LibriSpeech (MLS) and adapted for training TTS models, consisting of audiobooks in seven languages: Dutch, French, German, Italian, Portuguese, Polish, and Spanish. Additionally, we provide the YourTTS model, a multi-lingual TTS model, trained using 3,176.13 hours from CML-TTS and also with 245.07 hours from LibriTTS, in English. Our purpose in creating this dataset is to open up new research possibilities in the TTS area for multi-lingual models. The dataset is publicly available under the CC-BY 4.0 license1.