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。

GigaSpeech 2：用于低资源语种的演进、大规模、多领域的 ASR 语料库的自动爬取、转写和优化

GigaSpeech 2: An Evolving, Large-Scale and Multi-domain ASR Corpus for  Low-Resource Languages with Automated Crawling, Transcription and Refinement

In contemporary computer vision applications, particularly image
classification, architectural backbones pre-trained on large datasets like
ImageNet are commonly employed as feature extractors. Despite the widespread
use of these pre-trained convolutional neural networks (CNNs), there remains a
gap in understanding the performance of various resource-efficient backbones
across diverse domains and dataset sizes. Our study systematically evaluates
multiple lightweight, pre-trained CNN backbones under consistent training
settings across a variety of datasets, including natural images, medical
images, galaxy images, and remote sensing images. This comprehensive analysis
aims to aid machine learning practitioners in selecting the most suitable
backbone for their specific problem, especially in scenarios involving small
datasets where fine-tuning a pre-trained network is crucial. Even though
attention-based architectures are gaining popularity, we observed that they
tend to perform poorly under low data finetuning tasks compared to CNNs. We
also observed that some CNN architectures such as ConvNeXt, RegNet and
EfficientNet performs well compared to others on a diverse set of domains
consistently. Our findings provide actionable insights into the performance
trade-offs and effectiveness of different backbones, facilitating informed
decision-making in model selection for a broad spectrum of computer vision
domains. Our code is available here: this https URL

该研究评估了多个轻量级、预训练的 CNN 骨干网络在各种不同数据集上的性能，包括自然图像、医学图像、星系图像和遥感图像，并提供了关于计算机视觉领域不同骨干网络性能和有效性的可行见解，为模型选择提供指导。