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20142021
most citedAchieving Human Parity in Conversational Speech Recognition

477 citations · 1.7k across the 21 of their papers we have counts for

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Showing 2020Show all

6 papers · 1 filter

eess.AS2020

Benchmarking LF-MMI, CTC and RNN-T Criteria for Streaming ASR

Xiaohui Zhang, Frank Zhang, Chunxi Liu +8

In this work, to measure the accuracy and efficiency for a latency-controlled streaming automatic speech recognition (ASR) application, we perform comprehensive evaluations on thre…

cs.CL2020★ 1 cited

Improving RNN Transducer Based ASR with Auxiliary Tasks

Chunxi Liu, Frank Zhang, Duc Le +3

End-to-end automatic speech recognition (ASR) models with a single neural network have recently demonstrated state-of-the-art results compared to conventional hybrid speech recogni…

cs.CL2020★ 5 cited

Contextualizing ASR Lattice Rescoring with Hybrid Pointer Network Language Model

Da-Rong Liu, Chunxi Liu, Frank Zhang +3

Videos uploaded on social media are often accompanied with textual descriptions. In building automatic speech recognition (ASR) systems for videos, we can exploit the contextual in…

eess.AS2020★ 2 cited

Faster, Simpler and More Accurate Hybrid ASR Systems Using Wordpieces

Frank Zhang, Yongqiang Wang, Xiaohui Zhang +3

In this work, we first show that on the widely used LibriSpeech benchmark, our transformer-based context-dependent connectionist temporal classification (CTC) system produces state…

eess.AS2020

Large scale weakly and semi-supervised learning for low-resource video ASR

Kritika Singh, Vimal Manohar, Alex Xiao +7

Many semi- and weakly-supervised approaches have been investigated for overcoming the labeling cost of building high quality speech recognition systems. On the challenging task of…

cs.CV2020

On Compositions of Transformations in Contrastive Self-Supervised Learning

Mandela Patrick, Yuki M. Asano, Polina Kuznetsova +4

In the image domain, excellent representations can be learned by inducing invariance to content-preserving transformations via noise contrastive learning. In this paper, we general…