# CSS10: A Collection of Single Speaker Speech Datasets for 10 Languages

arXiv: https://arxiv.org/abs/1903.11269 · HTML: https://mlsanity.com/paper/1903.11269/

## Key facts

- Cited by 54 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Mar 2019.

## Built on by (most cited first, 30 of 54)

- [A Survey on Neural Speech Synthesis](https://mlsanity.com/paper/2106.15561/) (2021)
- [Textless Speech-to-Speech Translation on Real Data](https://mlsanity.com/paper/2112.08352/) (2021)
- [Neural Analysis and Synthesis: Reconstructing Speech from Self-Supervised Representations](https://mlsanity.com/paper/2110.14513/) (2021)
- [UnitY: Two-pass Direct Speech-to-speech Translation with Discrete Units](https://mlsanity.com/paper/2212.08055/) (2022)
- [SpeechMatrix: A Large-Scale Mined Corpus of Multilingual Speech-to-Speech Translations](https://mlsanity.com/paper/2211.04508/) (2022)
- [SwissDial: Parallel Multidialectal Corpus of Spoken Swiss German](https://mlsanity.com/paper/2103.11401/) (2021)
- [Language-Agnostic Meta-Learning for Low-Resource Text-to-Speech with Articulatory Features](https://mlsanity.com/paper/2203.03191/) (2022)
- [Face-Dubbing++: Lip-Synchronous, Voice Preserving Translation of Videos](https://mlsanity.com/paper/2206.04523/) (2022)
- [BLASER: A Text-Free Speech-to-Speech Translation Evaluation Metric](https://mlsanity.com/paper/2212.08486/) (2022)
- [HUI-Audio-Corpus-German: A high quality TTS dataset](https://mlsanity.com/paper/2106.06309/) (2021)
- [Low-Resource Multilingual and Zero-Shot Multispeaker TTS](https://mlsanity.com/paper/2210.12223/) (2022)
- [Unsupervised Text-to-Speech Synthesis by Unsupervised Automatic Speech Recognition](https://mlsanity.com/paper/2203.15796/) (2022)
- [MOSEL: 950,000 Hours of Speech Data for Open-Source Speech Foundation Model Training on EU Languages](https://mlsanity.com/paper/2410.01036/) (2024)
- [Exact Prosody Cloning in Zero-Shot Multispeaker Text-to-Speech](https://mlsanity.com/paper/2206.12229/) (2022)
- [Textless Unit-to-Unit training for Many-to-Many Multilingual Speech-to-Speech Translation](https://mlsanity.com/paper/2308.01831/) (2023)
- [Assessing Evaluation Metrics for Speech-to-Speech Translation](https://mlsanity.com/paper/2110.13877/) (2021)
- [ASR data augmentation in low-resource settings using cross-lingual multi-speaker TTS and cross-lingual voice conversion](https://mlsanity.com/paper/2204.00618/) (2022)
- [Learning to Speak from Text: Zero-Shot Multilingual Text-to-Speech with Unsupervised Text Pretraining](https://mlsanity.com/paper/2301.12596/) (2023)
- [Meta Learning Text-to-Speech Synthesis in over 7000 Languages](https://mlsanity.com/paper/2406.06403/) (2024)
- [Finnish Dialect Identification: The Effect of Audio and Text](https://mlsanity.com/paper/2111.03800/) (2021)
- [Scribosermo: Fast Speech-to-Text models for German and other Languages](https://mlsanity.com/paper/2110.07982/) (2021)
- [EMNS /Imz/ Corpus: An emotive single-speaker dataset for narrative storytelling in games, television and graphic novels](https://mlsanity.com/paper/2305.13137/) (2023)
- [Towards Quantifying and Reducing Language Mismatch Effects in Cross-Lingual Speech Anti-Spoofing](https://mlsanity.com/paper/2409.08346/) (2024)
- [Review of end-to-end speech synthesis technology based on deep learning](https://mlsanity.com/paper/2104.09995/) (2021)
- [Continual Speaker Adaptation for Text-to-Speech Synthesis](https://mlsanity.com/paper/2103.14512/) (2021)
- [A Toolbox for Construction and Analysis of Speech Datasets](https://mlsanity.com/paper/2104.04896/) (2021)
- [PoeticTTS -- Controllable Poetry Reading for Literary Studies](https://mlsanity.com/paper/2207.05549/) (2022)
- [Multilingual Speech-to-Speech Translation into Multiple Target Languages](https://mlsanity.com/paper/2307.08655/) (2023)
- [SelfVC: Voice Conversion With Iterative Refinement using Self Transformations](https://mlsanity.com/paper/2310.09653/) (2023)
- [The Greek podcast corpus: Competitive speech models for low-resourced languages with weakly supervised data](https://mlsanity.com/paper/2406.15284/) (2024)

## Abstract (arXiv)

We describe our development of CSS10, a collection of single speaker speech datasets for ten languages. It is composed of short audio clips from LibriVox audiobooks and their aligned texts. To validate its quality we train two neural text-to-speech models on each dataset. Subsequently, we conduct Mean Opinion Score tests on the synthesized speech samples. We make our datasets, pre-trained models, and test resources publicly available. We hope they will be used for future speech tasks.