Ali, Zulfiqar and Seco De Herrera, Alba G and Mesallam, Tamer A and Muhammad, Ghulam (2023) Computer-based Blind Diagnostic System for Classification of Healthy and Disordered Voices. In: 2023 IEEE 36th International Symposium on Computer-Based Medical Systems (CBMS), 2023-06-22 - 2023-06-24, L'Aquila, Italy.
Ali, Zulfiqar and Seco De Herrera, Alba G and Mesallam, Tamer A and Muhammad, Ghulam (2023) Computer-based Blind Diagnostic System for Classification of Healthy and Disordered Voices. In: 2023 IEEE 36th International Symposium on Computer-Based Medical Systems (CBMS), 2023-06-22 - 2023-06-24, L'Aquila, Italy.
Ali, Zulfiqar and Seco De Herrera, Alba G and Mesallam, Tamer A and Muhammad, Ghulam (2023) Computer-based Blind Diagnostic System for Classification of Healthy and Disordered Voices. In: 2023 IEEE 36th International Symposium on Computer-Based Medical Systems (CBMS), 2023-06-22 - 2023-06-24, L'Aquila, Italy.
Abstract
A large population around the world is suffering from voice-related complications. Computer-based voice disorder detection systems can play a substantial role in the early detection of voice disorders by providing complementary information to early-career otolaryngologists and general practitioners. However, various studies have concluded that the recording environment of voice samples affects disorder detection. This influence of the recording environment is a major obstacle in developing such systems when a local voice disorder database is not available. In addition, sometimes the number of samples is not sufficient for training the system. To overcome these issues, a blind detection system for voice disorders is designed and implemented in this study. Hence, without any prior knowledge of voice disorders, the proposed system has the ability to detect those disorders. The developed system relies only on healthy voice samples which can be recorded locally in the desired environment. The generation of a reference model for healthy subjects and decision criteria to detect voice disorders are two major tasks in the proposed systems. These tasks are implemented with two different types of speech features. Moreover, the unsupervised reference model is created by using DBSCAN and k-means algorithms. The overall performance of the system is 74.9 % in terms of the geometric mean of sensitivity and specificity. The results of the proposed system are encouraging and better than the performance of Multidimensional Voice Program (MDVP) parameters which are widely used for disorder assessment by otolaryngologists in clinics.
Item Type: | Conference or Workshop Item (Paper) |
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Uncontrolled Keywords: | Blind voice disease detection; DBSCAN; judgment reference model; objective analysis; unsupervised learning; vocal fold disorders |
Divisions: | Faculty of Science and Health Faculty of Science and Health > Computer Science and Electronic Engineering, School of |
SWORD Depositor: | Unnamed user with email elements@essex.ac.uk |
Depositing User: | Unnamed user with email elements@essex.ac.uk |
Date Deposited: | 09 Nov 2023 21:40 |
Last Modified: | 16 May 2024 21:58 |
URI: | http://repository.essex.ac.uk/id/eprint/36810 |
Available files
Filename: Computer-based_Blind_Diagnostic_System_for_Classification_of_Healthy_and_Disordered_Voices.pdf