Research and Application Analysis on Key Problems of Automatic Speech Recognition for Dysarthria

At present, automatic speech recognition technology has developed rapidly, and the recognition accuracy of Automatic Speech Recognition (ASR) based on deep learning has been very high. Therefore, the speech recognition problem of patients with dysarthria has been paid more attention in recent years. However, due to the particularity of dysarthria patients and the strong variability of their speech, the relevant available datasets are very scarce, and it is difficult to adapt to the current general recognition model. In order to promote the development and progress of this field, based on a large number of literature research, this paper summarizes the construction of data sets, the solution of speech variability, and the key issues and development trends of multi-feature speech data fusion. At the same time, it lists the current application of automatic speech recognition for dysarticulation in some fields. It is hoped that the need of social communication and intelligent life of patients with dysarthria who exist as a minority group can be solved as soon as possible.

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Publication Details

Journal
Applied and Computational Engineering
Published
2024-12-19
DOI
https://doi.org/10.54254/2755-2721/2025.18486
Primary Topic
Medical Research and Treatments
Type
article
Field-Weighted Citation Impact
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article

Research and Application Analysis on Key Problems of Automatic Speech Recognition for Dysarthria

Jie Yi
Applied and Computational Engineering
Medical Research and Treatments
article

Research and Application Analysis on Key Problems of Automatic Speech Recognition for Dysarthria

Jie Yi
article en

Abstract

At present, automatic speech recognition technology has developed rapidly, and the recognition accuracy of Automatic Speech Recognition (ASR) based on deep learning has been very high. Therefore, the speech recognition problem of patients with dysarthria has been paid more attention in recent years. However, due to the particularity of dysarthria patients and the strong variability of their speech, the relevant available datasets are very scarce, and it is difficult to adapt to the current general recognition model. In order to promote the development and progress of this field, based on a large number of literature research, this paper summarizes the construction of data sets, the solution of speech variability, and the key issues and development trends of multi-feature speech data fusion. At the same time, it lists the current application of automatic speech recognition for dysarticulation in some fields. It is hoped that the need of social communication and intelligent life of patients with dysarthria who exist as a minority group can be solved as soon as possible.

Applied and Computational EngineeringVol. 115(1)
Zhengzhou University of Light Industry (CN)
Quality Education
Openalex Percentile: Top 56%
Medical Research and Treatments
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