activity
20162021
most citedFrom Senones to Chenones: Tied Context-Dependent Graphemes for Hybrid Speech Recognition

9 citations · 18 across the 8 of their papers we have counts for

collaborators

13 papers

eess.AS20211 cited

Kaizen: Continuously improving teacher using Exponential Moving Average for semi-supervised speech recognition

Vimal Manohar, Tatiana Likhomanenko, Qiantong Xu +5

In this paper, we introduce the Kaizen framework that uses a continuously improving teacher to generate pseudo-labels for semi-supervised speech recognition (ASR). The proposed app…

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.CL20201 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.CL20205 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.AS20202 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…