Detection and Classification of Speech Disorder using FOA-SCNet
K. Sujigarasharma, M. Lawanya Shri, E. Gangadevi, Rajesh Kumar Dhanaraj, C. Narmatha, Balamurugan Balusamy
Abstract
Speech disorders have turned out a great brunt on the ability of speech. The persons have speech disorders were struggling with communication and emotional expressions. The proposed work detects and classifies the speech disorders using combined CNN and SVM techniques. To improve the prediction accuracy of the proposed model, we use the Fruitfly optimization algorithm for improving the convergence rate and accuracy results. The study mainly focuses on young people in which the speech disorders are prevalent. The machine algorithm is utilized to predict a person has healthy or pathological voice accurately.
Topics & Concepts
Support vector machineComputer scienceSpeech recognitionSpeech disorderConvergence (economics)Artificial intelligenceVoice activity detectionSpeech processingMachine learningAudiologyMedicineEconomicsEconomic growthCOVID-19 diagnosis using AIArtificial Intelligence in HealthcareBrain Tumor Detection and Classification