JVS corpus: free Japanese multi-speaker voice corpus

2019/08/17 by Shinnosuke Takamichi, Kentaro Mitsui, Takamichi, Shinnosuke +9 · 9 citations
Computer Science · Psychology · #Audio and Speech Processing (eess.AS) #FOS: Computer and information sciences #FOS: Electrical engineering #Natural Language Processing Techniques #Phonetics and Phonology Research #Sound (cs.SD) #Speech Recognition and Synthesis #electronic engineering #information engineering

paper · pdf · doi:10.48550/arxiv.1908.06248

openalex publication_date 2019/08/17 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

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.

Citations

• Deep Neural Networks for Acoustic Modeling in Speech Recognition: The Shared Views of Four Research Groups
• Sampling-based speech parameter generation using moment-matching networks
• DNN-based Speaker Embedding Using Subjective Inter-speaker Similarity for Multi-speaker Modeling in Speech Synthesis

Cited by

• SpeechLLM-as-Judges: Towards General and Interpretable Speech Quality Evaluation
• MixedG2P-T5: G2P-free Speech Synthesis for Mixed-script texts using Speech Self-Supervised Learning and Language Model
• Active Learning for Text-to-Speech Synthesis with Informative Sample Collection
• QHARMA-GAN: Quasi-Harmonic Neural Vocoder based on Autoregressive Moving Average Model
• Voice Conversion for Likability Control via Automated Rating of Speech Synthesis Corpora
• SpeechAccentLLM: A Unified Framework for Foreign Accent Conversion and Text to Speech
• Mitigating Language Mismatch in SSL-Based Speaker Anonymization
• Transcript-Prompted Whisper with Dictionary-Enhanced Decoding for Japanese Speech Annotation
• TTSOps: A Closed-Loop Corpus Optimization Framework for Training Multi-Speaker TTS Models from Dark Data

Related

• JSUT corpus: free large-scale Japanese speech corpus for end-to-end speech synthesis
• Coco-Nut: Corpus of Japanese Utterance and Voice Characteristics Description for Prompt-based Control
• JNV Corpus: A Corpus of Japanese Nonverbal Vocalizations with Diverse Phrases and Emotions
• JSSS: free Japanese speech corpus for summarization and simplification
• JVS-MuSiC: Japanese multispeaker singing-voice corpus