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20202025
most citedRecent Progress in the CUHK Dysarthric Speech Recognition System

93 citations · 170 across the 9 of their papers we have counts for

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8 papers · 1 filter

eess.AS2025

Towards Effective and Efficient Non-autoregressive decoders for Conformer and LLM-based ASR using Block-based Attention Mask

Tianzi Wang, Xurong Xie, Zengrui Jin +9

Automatic speech recognition (ASR) systems often rely on autoregressive (AR) Transformer decoder architectures, which limit efficient inference parallelization due to their sequent…

eess.AS2023

Hyper-parameter Adaptation of Conformer ASR Systems for Elderly and Dysarthric Speech Recognition

Tianzi Wang, Shoukang Hu, Jiajun Deng +5

Automatic recognition of disordered and elderly speech remains highly challenging tasks to date due to data scarcity. Parameter fine-tuning is often used to exploit the large quant…

eess.AS2022

Exploiting Cross Domain Acoustic-to-articulatory Inverted Features For Disordered Speech Recognition

Shujie Hu, Shansong Liu, Xurong Xie +6

Articulatory features are inherently invariant to acoustic signal distortion and have been successfully incorporated into automatic speech recognition (ASR) systems for normal spee…

eess.AS202293 cited

Recent Progress in the CUHK Dysarthric Speech Recognition System

Shansong Liu, Mengzhe Geng, Shoukang Hu +5

Despite the rapid progress of automatic speech recognition (ASR) technologies in the past few decades, recognition of disordered speech remains a highly challenging task to date. D…

eess.AS2020

Bayesian Learning of LF-MMI Trained Time Delay Neural Networks for Speech Recognition

Shoukang Hu, Xurong Xie, Shansong Liu +5

Discriminative training techniques define state-of-the-art performance for automatic speech recognition systems. However, they are inherently prone to overfitting, leading to poor…

eess.AS2020

Audio-visual Multi-channel Integration and Recognition of Overlapped Speech

Jianwei Yu, Shi-Xiong Zhang, Bo Wu +6

Automatic speech recognition (ASR) technologies have been significantly advanced in the past few decades. However, recognition of overlapped speech remains a highly challenging tas…