期刊
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
卷 70, 期 -, 页码 -出版社
ELSEVIER SCI LTD
DOI: 10.1016/j.bspc.2021.102973
关键词
Voice pathology; Automatic detection; Convolutional neural network; Deep learning
资金
- National Natural Science Foundation of China [61862043]
- Natural Science Foundation of Guangdong Province of China [2021A1515011915]
The automatic diagnosis method based on speech signal analysis plays an important role in detecting and classifying pathological voices, with the proposed CS-PVC system focusing on differences between different pathological voices and healthy voices and showing potential for universal applicability.
The automatic diagnosis method based on speech signal analysis is able to realize the detection and classification of pathological voices. It plays an important role in the early diagnosis and auxiliary treatment of voice pathology, which effectively relief the discomfort of patients and reduce the workload of doctors. Therefore, the automatic diagnosis method based on speech signal analysis is of great research value. Meanwhile, high accuracy, high precision and stability are the pursuit goals. In this paper, a novel computer-aided assessment based on speech signal analysis for pathological voice classification (CS-PVC) system is proposed. This model focuses on the areas with large differences between different pathological voices and healthy voices, while ignore the negative impact of insignificant information on the performance of the model. Two databases were used in the experiments, one is the Saarbruecken Voice database (SVD), and the other is the self-built Shenzhen People's Hospital voice database (SZUPD). The pathological voice detection accuracy of the proposed system on the above two databases are 81.6% and 82.2% respectively. The experimental results show that the proposed framework is not data-dependence. In other words, it has the potential to be universally applicable in medical framework in the future.
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