160 citations · 288 across the 25 of their papers we have counts for
4 papers · 1 filter
FLEURS: Few-shot Learning Evaluation of Universal Representations of Speech
Alexis Conneau, Min Ma, Simran Khanuja +6
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…
Evaluating the Diversity, Equity and Inclusion of NLP Technology: A Case Study for Indian Languages
Simran Khanuja, Sebastian Ruder, Partha Talukdar
In order for NLP technology to be widely applicable, fair, and useful, it needs to serve a diverse set of speakers across the world's languages, be equitable, i.e., not unduly bias…
XTREME-S: Evaluating Cross-lingual Speech Representations
Alexis Conneau, Ankur Bapna, Yu Zhang +16
We introduce XTREME-S, a new benchmark to evaluate universal cross-lingual speech representations in many languages. XTREME-S covers four task families: speech recognition, classif…
mSLAM: Massively multilingual joint pre-training for speech and text
Ankur Bapna, Colin Cherry, Yu Zhang +6
We present mSLAM, a multilingual Speech and LAnguage Model that learns cross-lingual cross-modal representations of speech and text by pre-training jointly on large amounts of unla…