title: From Tens of Hours to Tens of Thousands: Scaling Back-Translation for
    Speech Recognition
summary: Recent advances in Automatic Speech Recognition (ASR) have been largely
  fueled by massive speech corpora. However, extending coverage to diverse
  languages with limited resources remains a formidable challenge. This paper
  introduces Speech Back-Translation, a scalable pipeline that improves
  multilingual ASR models by converting large-scale text corpora into synthetic
  speech via off-the-shelf text-to-speech (TTS) models. We demonstrate that just
  tens of hours of real transcribed speech can effectively train TTS models to
  generate synthetic speech at hundreds of times the original volume while
  maintaining high quality. To evaluate synthetic speech quality, we develop an
  intelligibility-based assessment framework and establish clear thresholds for
  when synthetic data benefits ASR training. Using Speech Back-Translation, we
  generate more than 500,000 hours of synthetic speech in ten languages and
  continue pre-training Whisper-large-v3, achieving average transcription error
  reductions of over 30\%. These results highlight the scalability and
  effectiveness of Speech Back-Translation for enhancing multilingual ASR
  systems.
paper_authors._id: 68351e269f4e0a0f048ea664
paper_authors.name: Tianduo Wang
paper_authors._id: 68351e269f4e0a0f048ea665
paper_authors.name: Lu Xu
paper_authors._id: 68351e269f4e0a0f048ea666
paper_authors.name: Wei Lu
paper_authors._id: 68351e269f4e0a0f048ea667
paper_authors.name: Shanbo Cheng
paper_title: From Tens of Hours to Tens of Thousands: Scaling Back-Translation for
    Speech Recognition
paper_summary: Recent advances in Automatic Speech Recognition (ASR) have been largely
  fueled by massive speech corpora. However, extending coverage to diverse
  languages with limited resources remains a formidable challenge. This paper
  introduces Speech Back-Translation, a scalable pipeline that improves
  multilingual ASR models by converting large-scale text corpora into synthetic
  speech via off-the-shelf text-to-speech (TTS) models. We demonstrate that just
  tens of hours of real transcribed speech can effectively train TTS models to
  generate synthetic speech at hundreds of times the original volume while
  maintaining high quality. To evaluate synthetic speech quality, we develop an
  intelligibility-based assessment framework and establish clear thresholds for
  when synthetic data benefits ASR training. Using Speech Back-Translation, we
  generate more than 500,000 hours of synthetic speech in ten languages and
  continue pre-training Whisper-large-v3, achieving average transcription error
  reductions of over 30\%. These results highlight the scalability and
  effectiveness of Speech Back-Translation for enhancing multilingual ASR
  systems.
paper_ai_summary: Speech Back-Translation enhances multilingual ASR systems by generating high-quality synthetic speech from text corpora, significantly reducing transcription errors.
paper_ai_keywords: Automatic Speech Recognition Speech Back-Translation multilingual ASR text-to-speech synthetic speech intelligibility-based assessment Whisper-large-v3 transcription error reduction