activity
20182025
most citedA Comparison of Label-Synchronous and Frame-Synchronous End-to-End Models for Speech Recognition

16 citations · 25 across the 9 of their papers we have counts for

collaborators
Showing cs.CLShow all

5 papers · 1 filter

cs.CL2023

CIF-PT: Bridging Speech and Text Representations for Spoken Language Understanding via Continuous Integrate-and-Fire Pre-Training

Linhao Dong, Zhecheng An, Peihao Wu +3

Speech or text representation generated by pre-trained models contains modal-specific information that could be combined for benefiting spoken language understanding (SLU) tasks. I…

cs.CL2022

Improving End-to-End Contextual Speech Recognition with Fine-Grained Contextual Knowledge Selection

Minglun Han, Linhao Dong, Zhenlin Liang +4

Nowadays, most methods in end-to-end contextual speech recognition bias the recognition process towards contextual knowledge. Since all-neural contextual biasing methods rely on ph…

cs.CL2020

CIF-based Collaborative Decoding for End-to-end Contextual Speech Recognition

Minglun Han, Linhao Dong, Shiyu Zhou +1

End-to-end (E2E) models have achieved promising results on multiple speech recognition benchmarks, and shown the potential to become the mainstream. However, the unified structure…

cs.CL2019

CIF: Continuous Integrate-and-Fire for End-to-End Speech Recognition

Linhao Dong, Bo Xu

In this paper, we propose a novel soft and monotonic alignment mechanism used for sequence transduction. It is inspired by the integrate-and-fire model in spiking neural networks a…

cs.CL20193 cited

Self-Attention Aligner: A Latency-Control End-to-End Model for ASR Using Self-Attention Network and Chunk-Hopping

Linhao Dong, Feng Wang, Bo Xu

Self-attention network, an attention-based feedforward neural network, has recently shown the potential to replace recurrent neural networks (RNNs) in a variety of NLP tasks. Howev…