1.6k citations · 2.4k across the 10 of their papers we have counts for
19 papers
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…
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…
SLAM: A Unified Encoder for Speech and Language Modeling via Speech-Text Joint Pre-Training
Ankur Bapna, Yu-an Chung, Nan Wu +7
Unsupervised pre-training is now the predominant approach for both text and speech understanding. Self-attention models pre-trained on large amounts of unannotated data have been h…
Larger-Scale Transformers for Multilingual Masked Language Modeling
Naman Goyal, Jingfei Du, Myle Ott +2
Recent work has demonstrated the effectiveness of cross-lingual language model pretraining for cross-lingual understanding. In this study, we present the results of two larger mult…
Large-Scale Self- and Semi-Supervised Learning for Speech Translation
Changhan Wang, Anne Wu, Juan Pino +3
In this paper, we improve speech translation (ST) through effectively leveraging large quantities of unlabeled speech and text data in different and complementary ways. We explore…