FLEURS: Few-shot Learning Evaluation of Universal Representations of Speech

• URL: http://arxiv.org/abs/2205.12446v1
• Date: Wed, 25 May 2022 02:29:03 GMT
• Title: FLEURS: Few-shot Learning Evaluation of Universal Representations of Speech
• Authors: Alexis Conneau, Min Ma, Simran Khanuja, Yu Zhang, Vera Axelrod, Siddharth Dalmia, Jason Riesa, Clara Rivera, Ankur Bapna
• Abstract summary: We introduce FLEURS, the Few-shot Learning Evaluation of Universal Representations of Speech benchmark. FLEURS is an n-way parallel speech dataset in 102 languages built on top of the machine translation FLoRes-101 benchmark.
• Score: 33.71744518887916
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We introduce FLEURS, the Few-shot Learning Evaluation of Universal Representations of Speech benchmark. FLEURS is an n-way parallel speech dataset in 102 languages built on top of the machine translation FLoRes-101 benchmark, with approximately 12 hours of speech supervision per language. FLEURS can be used for a variety of speech tasks, including Automatic Speech Recognition (ASR), Speech Language Identification (Speech LangID), Translation and Retrieval. In this paper, we provide baselines for the tasks based on multilingual pre-trained models like mSLAM. The goal of FLEURS is to enable speech technology in more languages and catalyze research in low-resource speech understanding.

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