Identification of Voice Disorders Using Long-Time Features and Support Vector Machine With Different Feature Reduction Methods
| Autor: | Mansour Vali, Meisam K. Arjmandi, Alireza Moqarehzadeh, Mohammad Mikaili, Mohammad Pooyan |
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| Rok vydání: | 2011 |
| Předmět: |
Adult
Male Support Vector Machine Adolescent Computer science Feature vector Speech recognition Feature selection Speech Acoustics Young Adult Speech and Hearing Feature (machine learning) Humans Aged Aged 80 and over Principal Component Analysis Voice Disorders Receiver operating characteristic business.industry Discriminant Analysis Pattern recognition Middle Aged LPN and LVN Linear discriminant analysis Support vector machine Otorhinolaryngology Pattern recognition (psychology) Principal component analysis Female Artificial intelligence business |
| Zdroj: | Journal of Voice. 25:e275-e289 |
| ISSN: | 0892-1997 |
| Popis: | Identification of voice disorders has a fundamental role in our life nowadays. Therefore, many of these diseases must be diagnosed at early stages of occurrence before they lead to a critical condition. Acoustic analysis can be used to identify voice disorders as a complementary technique with other traditional invasive methods, such as laryngoscopy. In this article, we followed an extensive study in the diagnosis of voice disorders using the statistical pattern recognition techniques. Finally, we proposed a combined scheme of feature reduction methods followed by pattern recognition methods to classify voice disorders. Six classifiers are used to evaluate feature vectors obtained by principal component analysis or linear discriminant analysis (LDA) as feature reduction methods. Furthermore, individual, forward, backward, and branch-and-bound methods are examined as feature selection methods. The performance of each combined scheme is evaluated in terms of the accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve (AUC). The experimental results denote that LDA along with support vector machine (SVM) has the best performance, with a recognition rate of 94.26% and AUC of 97.94%. Additionally, this structure has the lowest complexity in comparison with other architectures. Among feature selection methods, individual feature selection followed by SVM classifier shows the best recognition rate of 91.55% and AUC of 95.80%. |
| Databáze: | OpenAIRE |
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