Cross-Lingual Transfer for Speech Processing Using Acoustic Language Similarity
Peter Wu, Jiatong Shi, Yifan Zhong, Shinji Watanabe, Alan W. Black · 2021 IEEE Automatic Speech Recognition and Understanding Workshop (ASRU) · 2021
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.