Speech processing systems currently do not support the vast majority of languages, in part due to the lack of data in low-resource languages. Cross-lingual transfer offers a compelling way to help bridge this digital divide by incorporating high-resource data into low-resource systems. Current cross-lingual algorithms have shown success in text-based tasks and speech-related tasks over some low-resource languages. However, scaling up speech systems to support hundreds of low-resource languages remains unsolved. To help bridge this gap, we propose a language similarity approach that can efficiently identify acoustic cross-lingual transfer pairs across hundreds of languages. We demonstrate the effectiveness of our approach in language family classification, speech recognition, and speech synthesis tasks.

翻译：目前,语音处理系统不支持绝大多数语文,部分原因是缺乏低资源语言的数据。跨语言传输通过将高资源数据纳入低资源系统,为弥合这一数字鸿沟提供了一条令人信服的途径。目前的跨语言算法在一些低资源语言的基于文本的任务和与语言有关的任务中表现出成功。然而,扩大语音系统以支持数百种低资源语言的工作仍未解决。为了帮助弥合这一差距,我们提议采用类似语言的方法,有效地识别数百种语言的跨语言音频传输配对。我们展示了我们在语言家庭分类、语音识别和语音合成任务方面的做法的有效性。