Abstract
The voice pathology detection using automatic classification systems is a useful way to diagnose voice diseases. In this paper, we propose a novel tool to detect voice pathology based on an incremental possibilistic SVM-HMM method which can be applied to serval practical applications using non-stationary or a very large-scale data in purpose to reduce the memory issues faced during the storage of the kernel matrix. The proposed system includes the steps of using SVM to incrementally compute possibilitic probabilities and then they will be used by HMM in order to detect voice pathologies. We evaluated the proposed method on the task of the detection of voice pathologies using voices samples from the Massachusetts Eye and Ear Infirmary Voice and Speech Laboratory (MEEI) database. According to the detection rates obtained by our system, the performance sounds robust, efficient and speed applied to a task of voices pathology detection.
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Amami, R., Amami, R., Eleraky, H.A. (2019). An Incremental System for Voice Pathology Detection Combining Possibilistic SVM and HMM. In: MartÃn-Vide, C., Purver, M., Pollak, S. (eds) Statistical Language and Speech Processing. SLSP 2019. Lecture Notes in Computer Science(), vol 11816. Springer, Cham. https://doi.org/10.1007/978-3-030-31372-2_11
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DOI: https://doi.org/10.1007/978-3-030-31372-2_11
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