Department of Computer Science and Engineering, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Saveetha University, Chennai, Tamil Nadu, India.
With the demand for better, more user-friendly HMIs, voice recognition systems have risen in prominence in recent years. The use of computer-assisted vocal pathology categorization tools allows for the accurate detection of voice pathology diseases. By using these methods, vocal disorders may be diagnosed early on and treated accordingly. An effective Deep Learning-based tool for feature extraction-based vocal pathology identification is the goal of this project. This research presents the results of using EfficientNet, a pre-trained Convolutional Neural Network (CNN), on a speech pathology dataset in order to achieve the highest possible classification accuracy. An Artificial Rabbit Optimization Algorithm (AROA)-tuned set of parameters complements the model's mobNet building elements, which include a linear stack of divisible convolution and max-pooling layers activated by Swish. In order to make the suggested approach applicable to a broad variety of voice disorder problems, this study also suggests a unique training method along with several training methodologies. One speech database, the Saarbrücken voice database (SVD), has been used to test the proposed technology. Using up to 96% accuracy, the experimental findings demonstrate that the suggested CNN approach is capable of detecting speech pathologies. The suggested method demonstrates great potential for use in real-world clinical settings, where it may provide accurate classifications in as little as three seconds and expedite automated diagnosis and treatment.
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Manikandan J
Manikandan J
Department of Electronics and Communication Engineering, Sri Sairam Engineering College, Chennai, India.
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Cite this article
Manikandan J, Kayalvizhi K, Yuvaraj Nachimuthu and Jeena R, “AROA based Pre-trained Model of Convolutional Neural Network for Voice Pathology Detection and Classification", pp. 463-471, April 2024. doi: 10.53759/7669/jmc202404044.
