VoxPopuli: A Large-Scale Multilingual Speech Corpus for Representation Learning, Semi-Supervised Learning and Interpretation

Autor:	Wang, Changhan, Rivière, Morgane, Lee, Ann, Wu, Anne, Talnikar, Chaitanya, Haziza, Daniel, Williamson, Mary, Pino, Juan, Dupoux, Emmanuel
Rok vydání:	2021
Předmět:	Computer Science - Computation and Language Electrical Engineering and Systems Science - Audio and Speech Processing
Druh dokumentu:	Working Paper
Popis:	We introduce VoxPopuli, a large-scale multilingual corpus providing 100K hours of unlabelled speech data in 23 languages. It is the largest open data to date for unsupervised representation learning as well as semi-supervised learning. VoxPopuli also contains 1.8K hours of transcribed speeches in 16 languages and their aligned oral interpretations into 5 other languages totaling 5.1K hours. We provide speech recognition baselines and validate the versatility of VoxPopuli unlabelled data in semi-supervised learning under challenging out-of-domain settings. We will release the corpus at https://github.com/facebookresearch/voxpopuli under an open license. Comment: Accepted to ACL 2021 (long paper)
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2101.00390 Zobrazit plný text záznamu View this record from Arxiv