16 citations · 23 across the 7 of their papers we have counts for
7 papers
Conformer Based Elderly Speech Recognition System for Alzheimer's Disease Detection
Tianzi Wang, Jiajun Deng, Mengzhe Geng +7
Early diagnosis of Alzheimer's disease (AD) is crucial in facilitating preventive care to delay further progression. This paper presents the development of a state-of-the-art Confo…
Exploring linguistic feature and model combination for speech recognition based automatic AD detection
Yi Wang, Tianzi Wang, Zi Ye +5
Early diagnosis of Alzheimer's disease (AD) is crucial in facilitating preventive care and delay progression. Speech based automatic AD screening systems provide a non-intrusive an…
Speaker Adaptation Using Spectro-Temporal Deep Features for Dysarthric and Elderly Speech Recognition
Mengzhe Geng, Xurong Xie, Zi Ye +5
Despite the rapid progress of automatic speech recognition (ASR) technologies targeting normal speech in recent decades, accurate recognition of dysarthric and elderly speech remai…
On-the-Fly Feature Based Rapid Speaker Adaptation for Dysarthric and Elderly Speech Recognition
Mengzhe Geng, Xurong Xie, Rongfeng Su +7
Accurate recognition of dysarthric and elderly speech remain challenging tasks to date. Speaker-level heterogeneity attributed to accent or gender, when aggregated with age and spe…
Spectro-Temporal Deep Features for Disordered Speech Assessment and Recognition
Mengzhe Geng, Shansong Liu, Jianwei Yu +6
Automatic recognition of disordered speech remains a highly challenging task to date. Sources of variability commonly found in normal speech including accent, age or gender, when f…
Bayesian Transformer Language Models for Speech Recognition
Boyang Xue, Jianwei Yu, Junhao Xu +6
State-of-the-art neural language models (LMs) represented by Transformers are highly complex. Their use of fixed, deterministic parameter estimates fail to account for model uncert…
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…