261 citations · 301 across the 14 of their papers we have counts for
9 papers · 1 filter
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…
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…
Streaming Attention-Based Models with Augmented Memory for End-to-End Speech Recognition
Ching-Feng Yeh, Yongqiang Wang, Yangyang Shi +4
Attention-based models have been gaining popularity recently for their strong performance demonstrated in fields such as machine translation and automatic speech recognition. One m…
Transformer in action: a comparative study of transformer-based acoustic models for large scale speech recognition applications
Yongqiang Wang, Yangyang Shi, Frank Zhang +4
In this paper, we summarize the application of transformer and its streamable variant, Emformer based acoustic model for large scale speech recognition applications. We compare the…
Emformer: Efficient Memory Transformer Based Acoustic Model For Low Latency Streaming Speech Recognition
Yangyang Shi, Yongqiang Wang, Chunyang Wu +5
This paper proposes an efficient memory transformer Emformer for low latency streaming speech recognition. In Emformer, the long-range history context is distilled into an augmente…
Weak-Attention Suppression For Transformer Based Speech Recognition
Yangyang Shi, Yongqiang Wang, Chunyang Wu +5
Transformers, originally proposed for natural language processing (NLP) tasks, have recently achieved great success in automatic speech recognition (ASR). However, adjacent acousti…
Streaming Transformer-based Acoustic Models Using Self-attention with Augmented Memory
Chunyang Wu, Yongqiang Wang, Yangyang Shi +2
Transformer-based acoustic modeling has achieved great suc-cess for both hybrid and sequence-to-sequence speech recogni-tion. However, it requires access to the full sequence, and…
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…
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…