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

arXiv: https://arxiv.org/abs/2103.14736 · HTML: https://mlsanity.com/paper/2103.14736/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 20 references that are in the map.
- First posted on arXiv 26 Mar 2021.

## Builds on (most cited first, 5 of 5)

- [Common Voice: A Massively-Multilingual Speech Corpus](https://mlsanity.com/paper/1912.06670/) (2019)
- [Deep Speech 2: End-to-End Speech Recognition in English and Mandarin](https://mlsanity.com/paper/1512.02595/) (2015)
- [TED-LIUM 3: twice as much data and corpus repartition for experiments on speaker adaptation](https://mlsanity.com/paper/1805.04699/) (2018)
- [Neural Speech Recognizer: Acoustic-to-Word LSTM Model for Large Vocabulary Speech Recognition](https://mlsanity.com/paper/1610.09975/) (2016)
- [Lessons from Building Acoustic Models with a Million Hours of Speech](https://mlsanity.com/paper/1904.01624/) (2019)

## Built on by (most cited first, 8 of 8)

- [InterBiasing: Boost Unseen Word Recognition through Biasing Intermediate Predictions](https://mlsanity.com/paper/2406.14890/) (2024)
- [Beyond Film Subtitles: Is YouTube the Best Approximation of Spoken Vocabulary?](https://mlsanity.com/paper/2410.03240/) (2024)
- [Exploration of Language Dependency for Japanese Self-Supervised Speech Representation Models](https://mlsanity.com/paper/2305.05201/) (2023)
- [WCTC-Biasing: Retraining-free Contextual Biasing ASR with Wildcard CTC-based Keyword Spotting and Inter-layer Biasing](https://mlsanity.com/paper/2506.01263/) (2025)
- [Learning to Jointly Transcribe and Subtitle for End-to-End Spontaneous Speech Recognition](https://mlsanity.com/paper/2210.07771/) (2022)
- [Supporting Experts with a Multimodal Machine-Learning-Based Tool for Human Behavior Analysis of Conversational Videos](https://mlsanity.com/paper/2402.11145/) (2024)
- [Dissecting Sensitivity to Training Language in Self-Supervised Speech Learning Using Neural Audio Codec Tokens](https://mlsanity.com/paper/2607.26350/) (2026)
- [Ruby-ASR: Evidence-Preserving Supervision for Joint Orthographic and Lexical-Reading Recognition](https://mlsanity.com/paper/2609.27289/) (2026)

## 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.