9 citations · 18 across the 8 of their papers we have counts for
6 papers · 1 filter
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…
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…
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…
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…
From Senones to Chenones: Tied Context-Dependent Graphemes for Hybrid Speech Recognition
Duc Le, Xiaohui Zhang, Weiyi Zheng +3
There is an implicit assumption that traditional hybrid approaches for automatic speech recognition (ASR) cannot directly model graphemes and need to rely on phonetic lexicons to g…
Multilingual Graphemic Hybrid ASR with Massive Data Augmentation
Chunxi Liu, Qiaochu Zhang, Xiaohui Zhang +3
Towards developing high-performing ASR for low-resource languages, approaches to address the lack of resources are to make use of data from multiple languages, and to augment the t…