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pro vyhledávání: '"Li, Changye"'
Automatic speech recognition (ASR) models trained on large amounts of audio data are now widely used to convert speech to written text in a variety of applications from video captioning to automated assistants used in healthcare and other domains. As
Externí odkaz:
http://arxiv.org/abs/2407.13982
Autor:
Ji, Jiaming, Wang, Kaile, Qiu, Tianyi, Chen, Boyuan, Zhou, Jiayi, Li, Changye, Lou, Hantao, Yang, Yaodong
Large language models (LLMs) may exhibit undesirable behaviors. Recent efforts have focused on aligning these models to prevent harmful generation. Despite these efforts, studies have shown that even a well-conducted alignment process can be easily c
Externí odkaz:
http://arxiv.org/abs/2406.06144
As artificial neural networks grow in complexity, understanding their inner workings becomes increasingly challenging, which is particularly important in healthcare applications. The intrinsic evaluation metrics of autoregressive neural language mode
Externí odkaz:
http://arxiv.org/abs/2406.02830
Useful Blunders: Can Automated Speech Recognition Errors Improve Downstream Dementia Classification?
\textbf{Objectives}: We aimed to investigate how errors from automatic speech recognition (ASR) systems affect dementia classification accuracy, specifically in the ``Cookie Theft'' picture description task. We aimed to assess whether imperfect ASR-g
Externí odkaz:
http://arxiv.org/abs/2401.05551
The evidence is growing that machine and deep learning methods can learn the subtle differences between the language produced by people with various forms of cognitive impairment such as dementia and cognitively healthy individuals. Valuable public d
Externí odkaz:
http://arxiv.org/abs/2302.07322
Linguistic anomalies detectable in spontaneous speech have shown promise for various clinical applications including screening for dementia and other forms of cognitive impairment. The feasibility of deploying automated tools that can classify langua
Externí odkaz:
http://arxiv.org/abs/2211.07430
Deep learning (DL) techniques involving fine-tuning large numbers of model parameters have delivered impressive performance on the task of discriminating between language produced by cognitively healthy individuals, and those with Alzheimer's disease
Externí odkaz:
http://arxiv.org/abs/2203.13397
Publikováno v:
In Separation and Purification Technology 30 October 2024 346
Publikováno v:
In Neuroscience Informatics March 2024 4(1)
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