mlsanityData to 7 Oct 2026

JVS corpus: free Japanese multi-speaker voice corpus

arXiv 1908.06248 ↗First posted 17 Aug 2019cs.SD eess.AScited by 20

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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.

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

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Citing papers per year: 2021: 3; 2022: 3; 2023: 1; 2024: 5; 2025: 6; 2026: 2.

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  1. 2024mHuBERT-147: A Compact Multilingual HuBERT Modelcited by 64
  2. 2021StarGANv2-VC: A Diverse, Unsupervised, Non-parallel Framework for Natural-Sounding Voice Conversioncited by 57
  3. 2021ESPnet2-TTS: Extending the Edge of TTS Researchcited by 22
  4. 2022Talking Face Generation with Multilingual TTScited by 18
  5. 2024Coding Speech through Vocal Tract Kinematicscited by 18
  6. 2022Heterogeneous Target Speech Separationcited by 13
  7. 2024RFWave: Multi-band Rectified Flow for Audio Waveform Reconstructioncited by 9
  8. 2025Voice Cloning: Comprehensive Surveycited by 4
  9. 2021High-Fidelity and Low-Latency Universal Neural Vocoder based on Multiband WaveRNN with Data-Driven Linear Prediction for Discrete Waveform Modelingcited by 3
  10. 2024A Pilot Study of GSLM-based Simulation of Foreign Accentuation Only Using Native Speech Corporacited by 2
  11. 2025Generative Adversarial Network based Voice Conversion: Techniques, Challenges, and Recent Advancementscited by 2
  12. 2024Analytic Study of Text-Free Speech Synthesis for Raw Audio using a Self-Supervised Learning Modelcited by 1
8 more
  1. 2025Transcript-Prompted Whisper with Dictionary-Enhanced Decoding for Japanese Speech Annotationcited by 1
  2. 2025UtterTune: LoRA-Based Target-Language Pronunciation Edit and Control in Multilingual Text-to-Speechcited by 1
  3. 2022SANE-TTS: Stable And Natural End-to-End Multilingual Text-to-Speechcited by 0
  4. 2023Automatic Tuning of Loss Trade-offs without Hyper-parameter Search in End-to-End Zero-Shot Speech Synthesiscited by 0
  5. 2025Empowering Global Voices: A Data-Efficient, Phoneme-Tone Adaptive Approach to High-Fidelity Speech Synthesiscited by 0
  6. 2025MixedG2P-T5: G2P-free Speech Synthesis for Mixed-script texts using Speech Self-Supervised Learning and Language Modelcited by 0
  7. 2026Training-Free Pronunciation Transcription via Text-Constrained Acoustic Rescoringcited by 0
  8. 2026Reconstructing the Vocal Tract with Differentiable Acoustic Simulationcited by 0