# FLEURS-R: A Restored Multilingual Speech Corpus for Generation Tasks

arXiv: https://arxiv.org/abs/2408.06227 · HTML: https://mlsanity.com/paper/2408.06227/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- Builds on 27 of its 30 references that are in the map.
- First posted on arXiv 12 Aug 2024.

## Builds on (most cited first, 27 of 27)

- [Conformer: Convolution-augmented Transformer for Speech Recognition](https://mlsanity.com/paper/2005.08100/) (2020)
- [SoundStream: An End-to-End Neural Audio Codec](https://mlsanity.com/paper/2107.03312/) (2021)
- [Neural Codec Language Models are Zero-Shot Text to Speech Synthesizers](https://mlsanity.com/paper/2301.02111/) (2023)
- [High Fidelity Neural Audio Compression](https://mlsanity.com/paper/2210.13438/) (2022)
- [LibriTTS: A Corpus Derived from LibriSpeech for Text-to-Speech](https://mlsanity.com/paper/1904.02882/) (2019)
- [WaveGrad: Estimating Gradients for Waveform Generation](https://mlsanity.com/paper/2009.00713/) (2020)
- [AudioLM: a Language Modeling Approach to Audio Generation](https://mlsanity.com/paper/2209.03143/) (2022)
- [MLS: A Large-Scale Multilingual Dataset for Speech Research](https://mlsanity.com/paper/2012.03411/) (2020)
- [FLEURS: Few-shot Learning Evaluation of Universal Representations of Speech](https://mlsanity.com/paper/2205.12446/) (2022)
- [Grad-TTS: A Diffusion Probabilistic Model for Text-to-Speech](https://mlsanity.com/paper/2105.06337/) (2021)
- [W2v-BERT: Combining Contrastive Learning and Masked Language Modeling for Self-Supervised Speech Pre-Training](https://mlsanity.com/paper/2108.06209/) (2021)
- [AudioPaLM: A Large Language Model That Can Speak and Listen](https://mlsanity.com/paper/2306.12925/) (2023)
- [Layer-wise Analysis of a Self-supervised Speech Representation Model](https://mlsanity.com/paper/2107.04734/) (2021)
- [YourTTS: Towards Zero-Shot Multi-Speaker TTS and Zero-Shot Voice Conversion for everyone](https://mlsanity.com/paper/2112.02418/) (2021)
- [Google USM: Scaling Automatic Speech Recognition Beyond 100 Languages](https://mlsanity.com/paper/2303.01037/) (2023)
- [Speak, Read and Prompt: High-Fidelity Text-to-Speech with Minimal Supervision](https://mlsanity.com/paper/2302.03540/) (2023)
- [PromptTTS: Controllable Text-to-Speech with Text Descriptions](https://mlsanity.com/paper/2211.12171/) (2022)
- [LibriTTS-R: A Restored Multi-Speaker Text-to-Speech Corpus](https://mlsanity.com/paper/2305.18802/) (2023)
- [InstructTTS: Modelling Expressive TTS in Discrete Latent Space with Natural Language Style Prompt](https://mlsanity.com/paper/2301.13662/) (2023)
- [PnG BERT: Augmented BERT on Phonemes and Graphemes for Neural TTS](https://mlsanity.com/paper/2103.15060/) (2021)
- [WaveFit: An Iterative and Non-autoregressive Neural Vocoder based on Fixed-Point Iteration](https://mlsanity.com/paper/2210.01029/) (2022)
- [DF-Conformer: Integrated architecture of Conv-TasNet and Conformer using linear complexity self-attention for speech enhancement](https://mlsanity.com/paper/2106.15813/) (2021)
- [CML-TTS A Multilingual Dataset for Speech Synthesis in Low-Resource Languages](https://mlsanity.com/paper/2306.10097/) (2023)
- [Maestro-U: Leveraging joint speech-text representation learning for zero supervised speech ASR](https://mlsanity.com/paper/2210.10027/) (2022)
- [SQuId: Measuring Speech Naturalness in Many Languages](https://mlsanity.com/paper/2210.06324/) (2022)
- [Extending Multilingual Speech Synthesis to 100+ Languages without Transcribed Data](https://mlsanity.com/paper/2402.18932/) (2024)
- [Attentive Temporal Pooling for Conformer-based Streaming Language Identification in Long-form Speech](https://mlsanity.com/paper/2202.12163/) (2022)

## Built on by (most cited first, 13 of 13)

- [AfriHuBERT: A self-supervised speech representation model for African languages](https://mlsanity.com/paper/2409.20201/) (2024)
- [Charting the Landscape of African NLP: Mapping Progress and Shaping the Road Ahead](https://mlsanity.com/paper/2505.21315/) (2025)
- [[b] = [d] - [t] + [p]: Self-supervised Speech Models Discover Phonological Vector Arithmetic](https://mlsanity.com/paper/2602.18899/) (2026)
- [UniCoM: A Universal Code-Switching Speech Generator](https://mlsanity.com/paper/2508.15244/) (2025)
- [Finite Scalar Quantization Enables Redundant and Transmission-Robust Neural Audio Compression at Low Bit-rates](https://mlsanity.com/paper/2509.09550/) (2025)
- [Sylber 2.0: A Universal Syllable Embedding](https://mlsanity.com/paper/2601.22306/) (2026)
- [Fotheidil: an Automatic Transcription System for the Irish Language](https://mlsanity.com/paper/2501.00509/) (2024)
- [Miipher-2: A Universal Speech Restoration Model for Million-Hour Scale Data Restoration](https://mlsanity.com/paper/2505.04457/) (2025)
- [OpenBibleTTS: Large-Scale Speech Resources and TTS Models for Low-Resource Languages](https://mlsanity.com/paper/2606.09553/) (2026)
- [SAGE-LD: Towards Scalable and Generalizable End-to-End Language Diarization via Simulated Data Augmentation](https://mlsanity.com/paper/2510.00582/) (2025)
- [BlasBench: An Open Benchmark for Irish Speech Recognition](https://mlsanity.com/paper/2604.10736/) (2026)
- [Saar-Voice: A Multi-Speaker Saarbr"ucken Dialect Speech Corpus](https://mlsanity.com/paper/2604.11803/) (2026)
- [ZONOS2 Technical Report](https://mlsanity.com/paper/2606.24320/) (2026)

## Abstract (arXiv)

This paper introduces FLEURS-R, a speech restoration applied version of the Few-shot Learning Evaluation of Universal Representations of Speech (FLEURS) corpus. FLEURS-R maintains an N-way parallel speech corpus in 102 languages as FLEURS, with improved audio quality and fidelity by applying the speech restoration model Miipher. The aim of FLEURS-R is to advance speech technology in more languages and catalyze research including text-to-speech (TTS) and other speech generation tasks in low-resource languages. Comprehensive evaluations with the restored speech and TTS baseline models trained from the new corpus show that the new corpus obtained significantly improved speech quality while maintaining the semantic contents of the speech. The corpus is publicly released via Hugging Face.