Abstract
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.
Abstract (translated)
最近的自动语音识别（ASR）进展主要得益于大规模语音语料库。然而，将覆盖范围扩展到资源有限的多种语言仍然是一个巨大的挑战。本文介绍了Speech Back-Translation，这是一种可扩展的工作流程，通过使用现成的文本转语音（TTS）模型将大型文本语料库转换为合成语音来改进多语言ASR模型。我们证明了仅用数十小时的实际录音可以有效训练TTS模型以生成比原始数据量大几百倍的高质量合成语音。为了评估合成语音的质量，我们开发了一个基于可理解性的评价框架，并确定了合成数据对ASR培训有益的确切阈值。通过使用Speech Back-Translation，我们在十种语言中生成了超过50万小时的合成语音并继续预训练Whisper-large-v3模型，在平均转录错误率上降低了超过30%。这些结果强调了Speech Back-Translation在增强多语言ASR系统中的可扩展性和有效性。
URL
https://arxiv.org/abs/2505.16972
