Abstract
Expanding the language coverage of speech technology has the potential to improve access to information for many more people. However, current speech technology is restricted to about one hundred languages which is a small fraction of the over 7,000 languages spoken around the world. The Massively Multilingual Speech (MMS) project increases the number of supported languages by 10-40x, depending on the task. The main ingredients are a new dataset based on readings of publicly available religious texts and effectively leveraging self-supervised learning. We built pre-trained wav2vec 2.0 models covering 1,406 languages, a single multilingual automatic speech recognition model for 1,107 languages, speech synthesis models for the same number of languages, as well as a language identification model for 4,017 languages. Experiments show that our multilingual speech recognition model more than halves the word error rate of Whisper on 54 languages of the FLEURS benchmark while being trained on a small fraction of the labeled data.
Abstract (translated)
扩大语音技术的语料范围有望改善更多人的信息获取。然而，当前语音技术仅涵盖约一百种语言，是全球超过7,000种语言的一小部分。大规模多语言语音(MMS)项目根据公开宗教文本的阅读，有效地利用了自监督学习。我们构建了一个覆盖1,406种语言的预训练wav2vec 2.0模型，为1,107种语言构建了一个单一的多语言自动语音识别模型，为相同数量的语料库构建了一个语言识别模型，以及为4,017种语言构建了一个语言分类模型。实验表明，我们的多语言语音识别模型在FLEURS基准测试中的Whisper单词错误率远远超过了的一半，而训练数据中的标注数据仅占一小部分。
URL
https://arxiv.org/abs/2305.13516
