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JTubeSpeech: corpus of Japanese speech collected from YouTube for speech recognition and speaker verification

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arxiv 2112.09323 v1 pith:6F4W3PUC submitted 2021-12-17 cs.SD eess.AS

Shinnosuke Takamichi , Ludwig Kürzinger , Takaaki Saeki , Sayaka Shiota , Shinji Watanabe This is my paper · ORCID
classification cs.SDeess.AS
keywords speechjapanesespeakercorpusrecognitionverificationautomaticcorpora
verification ladder T0 review T1 audit T2 compute T3 formal
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In this paper, we construct a new Japanese speech corpus called "JTubeSpeech." Although recent end-to-end learning requires large-size speech corpora, open-sourced such corpora for languages other than English have not yet been established. In this paper, we describe the construction of a corpus from YouTube videos and subtitles for speech recognition and speaker verification. Our method can automatically filter the videos and subtitles with almost no language-dependent processes. We consistently employ Connectionist Temporal Classification (CTC)-based techniques for automatic speech recognition (ASR) and a speaker variation-based method for automatic speaker verification (ASV). We build 1) a large-scale Japanese ASR benchmark with more than 1,300 hours of data and 2) 900 hours of data for Japanese ASV.

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Cited by 4 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. A Geometry-Limited Identification Floor and Its Consequences for Voice-Clone Attribution in Professional Voice Actors

    eess.AS 2026-07 conditional novelty 6.0 of 10

    On 1,168 professional voice actors, a misidentification floor in speaker embeddings survives calibration, normalization, and discriminative re-ranking, and the same floor makes fixed-threshold voice-clone attribution ...

  2. Active Learning for Text-to-Speech Synthesis with Informative Sample Collection

    cs.SD 2025-07 conditional novelty 6.0 of 10

    An iterative active learning pipeline that filters web speech data by predicted quality and redundancy produces a TTS corpus with better speaker coverage at the same size.

  3. Mitigating Language Mismatch in SSL-Based Speaker Anonymization

    eess.AS 2025-07 conditional novelty 5.0 of 10

    Fine-tuning an SSL content encoder on Japanese, especially when the encoder is pre-trained multilingually, makes anonymized Japanese and Mandarin speech much more intelligible while keeping speaker privacy at usable levels.

  4. Whale: Large-Scale multilingual ASR model with w2v-BERT and E-Branchformer with large speech data

    cs.CL 2025-06 conditional novelty 4.0 of 10

    Whale, a 1.87B-parameter ASR model combining w2v-BERT and E-Branchformer, reports 2.4% WER on Librispeech test-clean and 3.4% CER on CSJ eval3, beating Whisper large-v3 and OWSM v3.1 on those benchmarks.

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