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20172023
most citedMLS: A Large-Scale Multilingual Dataset for Speech Research

354 citations · 427 across the 7 of their papers we have counts for

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Showing 2020Show all

8 papers · 1 filter

eess.AS2020★ 354 cited

MLS: A Large-Scale Multilingual Dataset for Speech Research

Vineel Pratap, Qiantong Xu, Anuroop Sriram +2

This paper introduces Multilingual LibriSpeech (MLS) dataset, a large multilingual corpus suitable for speech research. The dataset is derived from read audiobooks from LibriVox an…

cs.LG2020

Self-training and Pre-training are Complementary for Speech Recognition

Qiantong Xu, Alexei Baevski, Tatiana Likhomanenko +5

Self-training and unsupervised pre-training have emerged as effective approaches to improve speech recognition systems using unlabeled data. However, it is not clear whether they l…

cs.LG2020

Rethinking Evaluation in ASR: Are Our Models Robust Enough?

Tatiana Likhomanenko, Qiantong Xu, Vineel Pratap +5

Is pushing numbers on a single benchmark valuable in automatic speech recognition? Research results in acoustic modeling are typically evaluated based on performance on a single da…

cs.CL2020

SlimIPL: Language-Model-Free Iterative Pseudo-Labeling

Tatiana Likhomanenko, Qiantong Xu, Jacob Kahn +2

Recent results in end-to-end automatic speech recognition have demonstrated the efficacy of pseudo-labeling for semi-supervised models trained both with Connectionist Temporal Clas…

cs.CL2020

Self-Training for End-to-End Speech Translation

Juan Pino, Qiantong Xu, Xutai Ma +2

One of the main challenges for end-to-end speech translation is data scarcity. We leverage pseudo-labels generated from unlabeled audio by a cascade and an end-to-end speech transl…

math.OC2020★ 7 cited

Spectral Frank-Wolfe Algorithm: Strict Complementarity and Linear Convergence

Lijun Ding, Yingjie Fei, Qiantong Xu +1

We develop a novel variant of the classical Frank-Wolfe algorithm, which we call spectral Frank-Wolfe, for convex optimization over a spectrahedron. The spectral Frank-Wolfe algori…