# JVS corpus: free Japanese multi-speaker voice corpus

arXiv: https://arxiv.org/abs/1908.06248 · HTML: https://mlsanity.com/paper/1908.06248/

## Key facts

- Cited by 20 papers in the map (data through 7 Oct 2026).
- First posted on arXiv 17 Aug 2019.

## Built on by (most cited first, 20 of 20)

- [mHuBERT-147: A Compact Multilingual HuBERT Model](https://mlsanity.com/paper/2406.06371/) (2024)
- [StarGANv2-VC: A Diverse, Unsupervised, Non-parallel Framework for Natural-Sounding Voice Conversion](https://mlsanity.com/paper/2107.10394/) (2021)
- [ESPnet2-TTS: Extending the Edge of TTS Research](https://mlsanity.com/paper/2110.07840/) (2021)
- [Talking Face Generation with Multilingual TTS](https://mlsanity.com/paper/2205.06421/) (2022)
- [Coding Speech through Vocal Tract Kinematics](https://mlsanity.com/paper/2406.12998/) (2024)
- [Heterogeneous Target Speech Separation](https://mlsanity.com/paper/2204.03594/) (2022)
- [RFWave: Multi-band Rectified Flow for Audio Waveform Reconstruction](https://mlsanity.com/paper/2403.05010/) (2024)
- [Voice Cloning: Comprehensive Survey](https://mlsanity.com/paper/2505.00579/) (2025)
- [High-Fidelity and Low-Latency Universal Neural Vocoder based on Multiband WaveRNN with Data-Driven Linear Prediction for Discrete Waveform Modeling](https://mlsanity.com/paper/2105.09856/) (2021)
- [A Pilot Study of GSLM-based Simulation of Foreign Accentuation Only Using Native Speech Corpora](https://mlsanity.com/paper/2407.11370/) (2024)
- [Generative Adversarial Network based Voice Conversion: Techniques, Challenges, and Recent Advancements](https://mlsanity.com/paper/2504.19197/) (2025)
- [Analytic Study of Text-Free Speech Synthesis for Raw Audio using a Self-Supervised Learning Model](https://mlsanity.com/paper/2412.03074/) (2024)
- [Transcript-Prompted Whisper with Dictionary-Enhanced Decoding for Japanese Speech Annotation](https://mlsanity.com/paper/2506.07646/) (2025)
- [UtterTune: LoRA-Based Target-Language Pronunciation Edit and Control in Multilingual Text-to-Speech](https://mlsanity.com/paper/2508.09767/) (2025)
- [SANE-TTS: Stable And Natural End-to-End Multilingual Text-to-Speech](https://mlsanity.com/paper/2206.12132/) (2022)
- [Automatic Tuning of Loss Trade-offs without Hyper-parameter Search in End-to-End Zero-Shot Speech Synthesis](https://mlsanity.com/paper/2305.16699/) (2023)
- [Empowering Global Voices: A Data-Efficient, Phoneme-Tone Adaptive Approach to High-Fidelity Speech Synthesis](https://mlsanity.com/paper/2504.07858/) (2025)
- [MixedG2P-T5: G2P-free Speech Synthesis for Mixed-script texts using Speech Self-Supervised Learning and Language Model](https://mlsanity.com/paper/2509.01391/) (2025)
- [Training-Free Pronunciation Transcription via Text-Constrained Acoustic Rescoring](https://mlsanity.com/paper/2609.30924/) (2026)
- [Reconstructing the Vocal Tract with Differentiable Acoustic Simulation](https://mlsanity.com/paper/2609.36737/) (2026)

## Abstract (arXiv)

Thanks to improvements in machine learning techniques, including deep learning, speech synthesis is becoming a machine learning task. To accelerate speech synthesis research, we are developing Japanese voice corpora reasonably accessible from not only academic institutions but also commercial companies. In 2017, we released the JSUT corpus, which contains 10 hours of reading-style speech uttered by a single speaker, for end-to-end text-to-speech synthesis. For more general use in speech synthesis research, e.g., voice conversion and multi-speaker modeling, in this paper, we construct the JVS corpus, which contains voice data of 100 speakers in three styles (normal, whisper, and falsetto). The corpus contains 30 hours of voice data including 22 hours of parallel normal voices. This paper describes how we designed the corpus and summarizes the specifications. The corpus is available at our project page.