24 citations · 114 across the 41 of their papers we have counts for
10 papers · 1 filter
An Empirical Study of Language Model Integration for Transducer based Speech Recognition
Huahuan Zheng, Keyu An, Zhijian Ou +3
Utilizing text-only data with an external language model (ELM) in end-to-end RNN-Transducer (RNN-T) for speech recognition is challenging. Recently, a class of methods such as dens…
CUSIDE: Chunking, Simulating Future Context and Decoding for Streaming ASR
Keyu An, Huahuan Zheng, Zhijian Ou +3
History and future contextual information are known to be important for accurate acoustic modeling. However, acquiring future context brings latency for streaming ASR. In this pape…
Exploiting Single-Channel Speech for Multi-Channel End-to-End Speech Recognition: A Comparative Study
Keyu An, Ji Xiao, Zhijian Ou
Recently, the end-to-end training approach for multi-channel ASR has shown its effectiveness, which usually consists of a beamforming front-end and a recognition back-end. However,…
Advancing CTC-CRF Based End-to-End Speech Recognition with Wordpieces and Conformers
Huahuan Zheng, Wenjie Peng, Zhijian Ou +1
Automatic speech recognition systems have been largely improved in the past few decades and current systems are mainly hybrid-based and end-to-end-based. The recently proposed CTC-…
Exploiting Single-Channel Speech For Multi-channel End-to-end Speech Recognition
Keyu An, Zhijian Ou
Recently, the end-to-end training approach for neural beamformer-supported multi-channel ASR has shown its effectiveness in multi-channel speech recognition. However, the integrati…
Deformable TDNN with adaptive receptive fields for speech recognition
Keyu An, Yi Zhang, Zhijian Ou
Time Delay Neural Networks (TDNNs) are widely used in both DNN-HMM based hybrid speech recognition systems and recent end-to-end systems. Nevertheless, the receptive fields of TDNN…
Efficient Neural Architecture Search for End-to-end Speech Recognition via Straight-Through Gradients
Huahuan Zheng, Keyu An, Zhijian Ou
Neural Architecture Search (NAS), the process of automating architecture engineering, is an appealing next step to advancing end-to-end Automatic Speech Recognition (ASR), replacin…
CAT: A CTC-CRF based ASR Toolkit Bridging the Hybrid and the End-to-end Approaches towards Data Efficiency and Low Latency
Keyu An, Hongyu Xiang, Zhijian Ou
In this paper, we present a new open source toolkit for speech recognition, named CAT (CTC-CRF based ASR Toolkit). CAT inherits the data-efficiency of the hybrid approach and the s…
Hybrid CTC-Attention based End-to-End Speech Recognition using Subword Units
Zhangyu Xiao, Zhijian Ou, Wei Chu +1
In this paper, we present an end-to-end automatic speech recognition system, which successfully employs subword units in a hybrid CTC-Attention based system. The subword units are…
Angular Softmax Loss for End-to-end Speaker Verification
Yutian Li, Feng Gao, Zhijian Ou +1
End-to-end speaker verification systems have received increasing interests. The traditional i-vector approach trains a generative model (basically a factor-analysis model) to extra…