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

Cross-lingual Transfer for Speech Processing using Acoustic Language Similarity