alexa Abstract | Survey on Speech Emotion Detection Using SVM and HMM
ISSN ONLINE(2320-9801) PRINT (2320-9798)

International Journal of Innovative Research in Computer and Communication Engineering
Open Access

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Research Article Open Access


Emotion detection in speech processing is one of the burning arenas in data mining field. Detecting the motion of the speech is not that easy as it seems to be. Many different researchers have tried their approach ing this field but accuracy is the major factor of the processing. Our basic problem is to detect the kind of emotion gets detected from a pitch file. This would be done with the help of the HMM algorithm which would identify the frequency parameters. Then after finding the exact length of the file, we will have to get into the predefined clusters. Mugging into the predefined clusters would be achieved by the SVM algorithm and each cluster will rollback to a result value. The exact cluster which would give us the maximum probilitical analysis of the file would be our target cluster. This work is done previously with the help of ANN algorithm and they have provided an accuracy of about 92.1%.Our problem would be increasing this accuracy ratio, in comparison to the ANN module.

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Author(s): Vijayalakshmi B, Dr.Sugumar Rajendran


Audio and Video Segmentation, Audio and video classification, Support Vector Machine (SVM), Auto associate Neural Network (AANN), Hidden Markov model (HMM), Speech pathology,Speech Therapy,Speech Therapy for Children,Speech Therapy for Adults,Speech Therapy Materials,Speech Therapy Exercise,Autism Speech Therapy,Speech and Language pathology,Communicate Speech pathology,Bilingual Speech pathology,Medical Speech pathology,Speech Impediment / speech disorder,Interventional Speech Therapy,Speech and Language Disorders

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