标题： Whisper 变得更强大：增强 Wav2Vec 2.0 以在低资源语言中实现更优的自动语音识别 显示英文标题

标题： Whisper Turns Stronger: Augmenting Wav2Vec 2.0 for Superior ASR in Low-Resource Languages

摘要： 在低资源语言中处理语音到文本和自动语音识别问题历来具有挑战性，这是由于验证数据集的稀缺性和方言的多样性。阿拉伯语、俄语和葡萄牙语就是这些困难的典型例子，由于这些语言在全球不同大陆的众多方言，它们属于低资源语言。此外，这些语言的口音和发音的多样性使ASR模型的成功变得更加复杂。随着深度学习和Transformer的日益流行，声学模型如著名的Wav2Vec2在语音识别领域相比最先进的方法取得了优越的性能。然而，尽管Wav2Vec2在传统方法上效率有所提高，但对于代表性不足的语言，其性能显著下降，尽管它需要的标记数据显著减少。本文介绍了一个端到端框架，通过数据增强技术来增强在Wav2Vec2上微调的ASR系统。为了验证我们框架的有效性，我们使用了Mozilla的Common Voice项目中的三个数据集（阿拉伯语、俄语和葡萄牙语）进行了详细的实验评估。此外，本文提出的框架对不同的变音符号表现出鲁棒性。最终，我们的方法优于两个之前的基线模型，即预训练的Wav2Vec2和著名的Whisper ASR模型，在词错误率方面平均相对提高了33.9%，在字符错误率方面相对提高了53.2%。 显示英文摘要

摘要： Approaching Speech-to-Text and Automatic Speech Recognition problems in low-resource languages is notoriously challenging due to the scarcity of validated datasets and the diversity of dialects. Arabic, Russian, and Portuguese exemplify these difficulties, being low-resource languages due to the many dialects of these languages across different continents worldwide. Moreover, the variety of accents and pronunciations of such languages complicate ASR models' success. With the increasing popularity of Deep Learning and Transformers, acoustic models like the renowned Wav2Vec2 have achieved superior performance in the Speech Recognition field compared to state-of-the-art approaches. However, despite Wav2Vec2's improved efficiency over traditional methods, its performance significantly declines for under-represented languages, even though it requires significantly less labeled data. This paper introduces an end-to-end framework that enhances ASR systems fine-tuned on Wav2Vec2 through data augmentation techniques. To validate our framework's effectiveness, we conducted a detailed experimental evaluation using three datasets from Mozilla's Common Voice project in Arabic, Russian, and Portuguese. Additionally, the framework presented in this paper demonstrates robustness to different diacritics. Ultimately, our approach outperforms two previous baseline models, which are the pre-trained Wav2Vec2 and the well-known Whisper ASR model, resulting in an average relative improvement of 33.9\% in Word Error Rate and a 53.2\% relative improvement in Character Error Rate.