mlsanityData to 7 Oct 2026

Construction of a Large-scale Japanese ASR Corpus on TV Recordings

arXiv 2103.14736 ↗First posted 26 Mar 2021cs.SD cs.CL eess.AScited by 8

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Abstract (arXiv)

This paper presents a new large-scale Japanese speech corpus for training automatic speech recognition (ASR) systems. This corpus contains over 2,000 hours of speech with transcripts built on Japanese TV recordings and their subtitles. We develop herein an iterative workflow to extract matching audio and subtitle segments from TV recordings based on a conventional method for lightly-supervised audio-to-text alignment. We evaluate a model trained with our corpus using an evaluation dataset built on Japanese TEDx presentation videos and confirm that the performance is better than that trained with the Corpus of Spontaneous Japanese (CSJ). The experiment results show the usefulness of our corpus for training ASR systems. This corpus is made public for the research community along with Kaldi scripts for training the models reported in this paper.

Key facts. Cited by 8 papers in the map (data through 7 Oct 2026). Builds on 5 of its 20 references that are in the map. First posted on arXiv 26 Mar 2021.

Builds on

5 of 20 references.

References · 5

  1. 2019Common Voice: A Massively-Multilingual Speech Corpuscited by 1,358
  2. 2015Deep Speech 2: End-to-End Speech Recognition in English and Mandarincited by 449
  3. 2018TED-LIUM 3: twice as much data and corpus repartition for experiments on speaker adaptationcited by 224
  4. 2016Neural Speech Recognizer: Acoustic-to-Word LSTM Model for Large Vocabulary Speech Recognitioncited by 33
  5. 2019Lessons from Building Acoustic Models with a Million Hours of Speechcited by 27

Built on by

Cited by year (2026 to 7 Oct)

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

8 papers.

  1. 2024InterBiasing: Boost Unseen Word Recognition through Biasing Intermediate Predictionscited by 5
  2. 2024Beyond Film Subtitles: Is YouTube the Best Approximation of Spoken Vocabulary?cited by 5
  3. 2023Exploration of Language Dependency for Japanese Self-Supervised Speech Representation Modelscited by 3
  4. 2025WCTC-Biasing: Retraining-free Contextual Biasing ASR with Wildcard CTC-based Keyword Spotting and Inter-layer Biasingcited by 3
  5. 2022Learning to Jointly Transcribe and Subtitle for End-to-End Spontaneous Speech Recognitioncited by 2
  6. 2024Supporting Experts with a Multimodal Machine-Learning-Based Tool for Human Behavior Analysis of Conversational Videoscited by 2
  7. 2026Dissecting Sensitivity to Training Language in Self-Supervised Speech Learning Using Neural Audio Codec Tokenscited by 0
  8. 2026Ruby-ASR: Evidence-Preserving Supervision for Joint Orthographic and Lexical-Reading Recognitioncited by 0