The evolution of speech technology has been spurred by the rapid increase in
dataset sizes. Traditional speech models generally depend on a large amount of
labeled training data, which is scarce for low-resource languages. This paper
presents GigaSpeech 2, a large-scale, multi-domain, multilingual speech
recognition corpus. It is designed for low-resource languages and does not rely
on paired speech and text data. GigaSpeech 2 comprises about 30,000 hours of
automatically transcribed speech, including Thai, Indonesian, and Vietnamese,
gathered from unlabeled YouTube videos. We also introduce an automated pipeline
for data crawling, transcription, and label refinement. Specifically, this
pipeline uses Whisper for initial transcription and TorchAudio for forced
alignment, combined with multi-dimensional filtering for data quality
assurance. A modified Noisy Student Training is developed to further refine
flawed pseudo labels iteratively, thus enhancing model performance.
Experimental results on our manually transcribed evaluation set and two public
test sets from Common Voice and FLEURS confirm our corpus's high quality and
broad applicability. Notably, ASR models trained on GigaSpeech 2 can reduce the
word error rate for Thai, Indonesian, and Vietnamese on our challenging and
realistic YouTube test set by 25% to 40% compared to the Whisper large-v3
model, with merely 10% model parameters. Furthermore, our ASR models trained on
Gigaspeech 2 yield superior performance compared to commercial services. We
believe that our newly introduced corpus and pipeline will open a new avenue
for low-resource speech recognition and significantly facilitate research in
this area.

这篇论文介绍了 GigaSpeech 2，一个为低资源语言设计的大规模、多领域、多语种的语音识别语料库，它不依赖于配对的语音和文本数据。该论文还介绍了一个自动化的数据爬取、转录和标签优化流程，以及通过修改的 Noisy Student Training 来进一步提高模型性能。实验结果证明了该语料库高质量和广泛适用性，并且相比于 Whisper large-v3 模型，基于 GigaSpeech 2 训练的 ASR 模型在泰语、印尼语和越南语的测试集上可以将词错误率降低 25% 至 40%。同时，与商业服务相比，基于 GigaSpeech 2 训练的 ASR 模型也能取得更好的性能。我们相信我们引入的语料库和流程将为低资源语音识别开辟新的研究途径并极大地促进该领域的研究。