Abstract
Voice problems influence numerous people these days, if any of their internal organs are infected. Voice pathologies may affect people who are elderly, frequently allergic, addicted to tobacco as well as singers, speakers, teachers and so on. Diagnosing those disorders at the earliest ahead of examining by specialists reduces the expensive treatment cost. Acoustic analysis plays a major part in identifying voice disorders. Examination techniques in recognizing voice pathologies can be intertwined with deep learning strategies to identify the voice pathology automatically and to accurately distinguish people who are healthy from people who are suffering with voice pathology. The fundamental purpose of this paper is to accomplish an initial study that elucidates the possibilities of using classification models in case of pathological voice recognition. Experiments were conducted using classifiers, and their performance metrics were evaluated. The result analysis shows that MLP performs well with 98% accuracy compared to other classifiers.
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Deepa, P., Khilar, R. (2022). Lecture Notes in Computer Science: Pathological Voice Recognition Based on Acoustic Phonatory Features. In: Mohanty, M.N., Das, S. (eds) Advances in Intelligent Computing and Communication. Lecture Notes in Networks and Systems, vol 430. Springer, Singapore. https://doi.org/10.1007/978-981-19-0825-5_9
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DOI: https://doi.org/10.1007/978-981-19-0825-5_9
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