From Tens of Hours to Tens of Thousands: Scaling Back-Translation for
  Speech Recognition
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.
68351e269f4e0a0f048ea664
Tianduo Wang
68351e269f4e0a0f048ea665
Lu Xu
68351e269f4e0a0f048ea666
Wei Lu
68351e269f4e0a0f048ea667
Shanbo Cheng
From Tens of Hours to Tens of Thousands: Scaling Back-Translation for
  Speech Recognition
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.
Speech Back-Translation enhances multilingual ASR systems by generating high-quality synthetic speech from text corpora, significantly reducing transcription errors.
Automatic Speech Recognition Speech Back-Translation multilingual ASR text-to-speech synthetic speech intelligibility-based assessment Whisper-large-v3 transcription error reduction