  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.
