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Cross-lingual Transfer for Speech Processing using Acoustic Language Similarity
- Publication Year :
- 2021
-
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.
Details
- Database :
- arXiv
- Publication Type :
- Report
- Accession number :
- edsarx.2111.01326
- Document Type :
- Working Paper