most citedDeCoAR 2.0: Deep Contextualized Acoustic Representations with Vector Quantization

58 citations · 70 across the 3 of their papers we have counts for

collaborators

5 papers

eess.AS202058 cited

DeCoAR 2.0: Deep Contextualized Acoustic Representations with Vector Quantization

Shaoshi Ling, Yuzong Liu

Recent success in speech representation learning enables a new way to leverage unlabeled data to train speech recognition model. In speech representation learning, a large amount o…

cs.CL2020

Transformer-Transducers for Code-Switched Speech Recognition

Siddharth Dalmia, Yuzong Liu, Srikanth Ronanki +1

We live in a world where 60% of the population can speak two or more languages fluently. Members of these communities constantly switch between languages when having a conversation…

eess.AS20209 cited

Streaming Language Identification using Combination of Acoustic Representations and ASR Hypotheses

Chander Chandak, Zeynab Raeesy, Ariya Rastrow +5

This paper presents our modeling and architecture approaches for building a highly accurate low-latency language identification system to support multilingual spoken queries for vo…

eess.AS2019

Deep Contextualized Acoustic Representations For Semi-Supervised Speech Recognition

Shaoshi Ling, Yuzong Liu, Julian Salazar +1

We propose a novel approach to semi-supervised automatic speech recognition (ASR). We first exploit a large amount of unlabeled audio data via representation learning, where we rec…

cs.CL20193 cited

End-to-end Anchored Speech Recognition

Yiming Wang, Xing Fan, I-Fan Chen +3

Voice-controlled house-hold devices, like Amazon Echo or Google Home, face the problem of performing speech recognition of device-directed speech in the presence of interfering bac…