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20222024
most citedLeveraging Pretrained Representations with Task-related Keywords for Alzheimer's Disease Detection

1 citations · 3 across the 7 of their papers we have counts for

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7 papers

cs.SD2024

Exploiting Audio-Visual Features with Pretrained AV-HuBERT for Multi-Modal Dysarthric Speech Reconstruction

Xueyuan Chen, Yuejiao Wang, Xixin Wu +4

Dysarthric speech reconstruction (DSR) aims to transform dysarthric speech into normal speech by improving the intelligibility and naturalness. This is a challenging task especiall…

eess.AS2023

Use of Speech Impairment Severity for Dysarthric Speech Recognition

Mengzhe Geng, Zengrui Jin, Tianzi Wang +7

A key challenge in dysarthric speech recognition is the speaker-level diversity attributed to both speaker-identity associated factors such as gender, and speech impairment severit…

eess.AS2023

A Hierarchical Regression Chain Framework for Affective Vocal Burst Recognition

Jinchao Li, Xixin Wu, Kaitao Song +3

As a common way of emotion signaling via non-linguistic vocalizations, vocal burst (VB) plays an important role in daily social interaction. Understanding and modeling human vocal…

eess.AS20231 cited

Leveraging Pretrained Representations with Task-related Keywords for Alzheimer's Disease Detection

Jinchao Li, Kaitao Song, Junan Li +5

With the global population aging rapidly, Alzheimer's disease (AD) is particularly prominent in older adults, which has an insidious onset and leads to a gradual, irreversible dete…

eess.AS2023

Confidence Score Based Speaker Adaptation of Conformer Speech Recognition Systems

Jiajun Deng, Xurong Xie, Tianzi Wang +6

Speaker adaptation techniques provide a powerful solution to customise automatic speech recognition (ASR) systems for individual users. Practical application of unsupervised model-…

cs.CL20221 cited

Bayesian Neural Network Language Modeling for Speech Recognition

Boyang Xue, Shoukang Hu, Junhao Xu +3

State-of-the-art neural network language models (NNLMs) represented by long short term memory recurrent neural networks (LSTM-RNNs) and Transformers are becoming highly complex. Th…