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20182022
most citedDoes Object Recognition Work for Everyone?

101 citations · 191 across the 14 of their papers we have counts for

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22 papers · 1 filter

cs.CL2022

Introducing Semantics into Speech Encoders

Derek Xu, Shuyan Dong, Changhan Wang +10

Recent studies find existing self-supervised speech encoders contain primarily acoustic rather than semantic information. As a result, pipelined supervised automatic speech recogni…

cs.CL20225 cited

Speech-to-Speech Translation For A Real-world Unwritten Language

Peng-Jen Chen, Kevin Tran, Yilin Yang +13

We study speech-to-speech translation (S2ST) that translates speech from one language into another language and focuses on building systems to support languages without standard te…

cs.CL20224 cited

SpeechMatrix: A Large-Scale Mined Corpus of Multilingual Speech-to-Speech Translations

Paul-Ambroise Duquenne, Hongyu Gong, Ning Dong +7

We present SpeechMatrix, a large-scale multilingual corpus of speech-to-speech translations mined from real speech of European Parliament recordings. It contains speech alignments…

cs.CL2022

Improving Speech-to-Speech Translation Through Unlabeled Text

Xuan-Phi Nguyen, Sravya Popuri, Changhan Wang +3

Direct speech-to-speech translation (S2ST) is among the most challenging problems in the translation paradigm due to the significant scarcity of S2ST data. While effort has been ma…

cs.CL20222 cited

Simple and Effective Unsupervised Speech Translation

Changhan Wang, Hirofumi Inaguma, Peng-Jen Chen +5

The amount of labeled data to train models for speech tasks is limited for most languages, however, the data scarcity is exacerbated for speech translation which requires labeled d…

cs.CL2022

Unified Speech-Text Pre-training for Speech Translation and Recognition

Yun Tang, Hongyu Gong, Ning Dong +8

We describe a method to jointly pre-train speech and text in an encoder-decoder modeling framework for speech translation and recognition. The proposed method incorporates four sel…