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

Alexis Conneau
Min Ma
Simran Khanuja
Yu Zhang
Siddharth Dalmia
Clara Rivera
IEEE Spoken Language Technology Workshop (SLT) (2022)
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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.

Research Areas