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.

通过对噪音广播档案的无监督的语音表示学习方法进行调查，我们的贡献包括发布两个数据集到研究社区以及共享受过训练的语音编码器，最终分享了 Maninka、Pular 和 Susu 等语言的首个语音识别模型，为服务于数字鸿沟中穷困落后的人群而提供了一种道路。

利用无线电存档进行低资源语音识别：面向文盲用户的智能虚拟助手

Using Radio Archives for Low-Resource Speech Recognition: Towards an  Intelligent Virtual Assistant for Illiterate Users

Unsupervised speech representation learning has shown remarkable success at
finding representations that correlate with phonetic structures and improve
downstream speech recognition performance. However, most research has been
focused on evaluating the representations in terms of their ability to improve
the performance of speech recognition systems on read English (e.g. Wall Street
Journal and LibriSpeech). This evaluation methodology overlooks two important
desiderata that speech representations should have: robustness to domain shifts
and transferability to other languages. In this paper we learn representations
from up to 8000 hours of diverse and noisy speech data and evaluate the
representations by looking at their robustness to domain shifts and their
ability to improve recognition performance in many languages. We find that our
representations confer significant robustness advantages to the resulting
recognition systems: we see significant improvements in out-of-domain transfer
relative to baseline feature sets and the features likewise provide
improvements in 25 phonetically diverse languages including tonal languages and
low-resource languages.

本文旨在研究无监督语音表示学习在具备鲁棒性和可迁移性方面的表现，通过使用多样性和嘈杂的语音数据学习表示，并在多个语言中验证其鲁棒性和可迁移性。结果显示，该方法相比基线特征集在跨域转移和 25 种不同语言的语音识别上均有显著提升。