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• W3211231337 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." @default.
• W3211231337 created "2021-11-08" @default.
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• W3211231337 date "2021-11-01" @default.
• W3211231337 modified "2023-09-24" @default.
• W3211231337 title "Cross-lingual Transfer for Speech Processing using Acoustic Language Similarity" @default.
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• W3211231337 doi "https://doi.org/10.48550/arxiv.2111.01326" @default.
• W3211231337 hasPublicationYear "2021" @default.
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