Cross-lingual Transfer for Speech Processing using Acoustic Language Similarity

• URL: http://arxiv.org/abs/2111.01326v1
• Date: Tue, 2 Nov 2021 01:55:17 GMT
• Title: Cross-lingual Transfer for Speech Processing using Acoustic Language Similarity
• Authors: Peter Wu, Jiatong Shi, Yifan Zhong, Shinji Watanabe, Alan W Black
• Abstract summary: Cross-lingual transfer offers a compelling way to help bridge this digital divide. Current cross-lingual algorithms have shown success in text-based tasks and speech-related tasks over some low-resource languages. We propose a language similarity approach that can efficiently identify acoustic cross-lingual transfer pairs across hundreds of languages.
• Score: 81.51206991542242
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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