利用无线电档案进行低资源语音识别：面向文盲用户的智能虚拟助理
Using Radio Archives for Low-Resource Speech Recognition: Towards an Intelligent Virtual Assistant for Illiterate Users
摘要：
对于全世界7亿文盲中的许多人来说，语音识别技术可以为他们提供宝贵的信息和服务。然而，最需要这项技术的人往往是最得不到服务的人。在许多国家，文盲往往只会说低资源语言，而语音技术发展所需的数据集非常稀缺。在本文中，我们调查的有效性无监督语音表示学习嘈杂的无线电广播档案，这是丰富的，即使在低资源的语言。我们做出三大贡献。首先，我们向研究社区发布两个数据集。第一个是西非广播语料库，包含142小时的10多种语言的音频，带有标记的验证子集。第二个是西非虚拟助理语音识别语料库，由10K四种语言的标记音频片段组成。接下来，我们分享西非wav2vec，一个在嘈杂的无线电语料库上训练的语音编码器，并将其与基线Facebook语音编码器进行比较，该编码器在更高质量的六倍数据上训练。我们表明，西非wav2vec执行类似的多语种语音识别任务的基线，并显着优于西非语言识别任务的基线。最后，我们分享了Maninka、Pular和Susu三种语言的首个语音识别模型，这些语言在七个以上的国家共有1000万人使用，其中六个国家的大多数成年人都是文盲。我们的贡献为伦理人工智能研究提供了一条前进的道路，以满足那些因数字鸿沟而处于最不利地位的人的需求。
英文摘要：
For many of the 700 million illiterate people around the world, speech recognition technology could provide a bridge to valuable information and services. Yet, those most in need of this technology are often the most underserved by it. In many countries, illiterate people tend to speak only low-resource languages, for which the datasets necessary for speech technology development are scarce. In this paper, we investigate the effectiveness of unsupervised speech representation learning on noisy radio broadcasting archives, which are abundant even in low-resource languages. We make three core contributions. First, we release two datasets to the research community. The first, West African Radio Corpus, contains 142 hours of audio in more than 10 languages with a labeled validation subset. The second, West African Virtual Assistant Speech Recognition Corpus, consists of 10K labeled audio clips in four languages. Next, we share West African wav2vec, a speech encoder trained on the noisy radio corpus, and compare it with the baseline Facebook speech encoder trained on six times more data of higher quality. We show that West African wav2vec performs similarly to the baseline on a multilingual speech recognition task, and significantly outperforms the baseline on a West African language identification task. Finally, we share the first-ever speech recognition models for Maninka, Pular and Susu, languages spoken by a combined 10 million people in over seven countries, including six where the majority of the adult population is illiterate. Our contributions offer a path forward for ethical AI research to serve the needs of those most disadvantaged by the digital divide.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
