alexa Survey on Knowledge Discovery in Speech Emotion Detection
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

Survey on Knowledge Discovery in Speech Emotion Detection

S.Jagadeesh Soundappan, Dr.R.Sugumar
  1. Research Scholar, Dept. of CSE, St. Peter’s University, Chennai, India
  2. Associate Professor, Dept. of CSE, Veltech Multitech Dr.RR Dr.SR Engineering College, Chennai, India
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Abstract

Knowledge discovery refers to finding some relevant information out of a bulk amount of data. Speech emotion recognition is one of the major areas in the knowledge based discovery. This research work has been carried out using four emotions namely sad happy angry and aggressive. This research work possesses two sections namely training and the testing part. The training part will consists of the updation of the speech files with the data base system. Once a file is uploaded, the system would extract the features of the speech file with an algorithm named MFCC. The MFCC algorithm would extract a feature vector out the speech file and then the maximum, minimum and average value of the feature vector would be saved into the database. The process would repeat itself again and again till the last category is not achieved. Once the training part is complete, the testing section would be initiated. The testing section would involve the classification process in which two classifiers would be used. The first classifier is neural networks whose back propagation feed forward neural network would be used for the processing. The BPNN is one the most affective classifier out of the available classifiers. The initial hidden layer in the BPNN process has been kept as 20 and minimum number of iterations is 5. Some sort of previous work has been also implemented before this research work getting proposed like use of BPNN for speech classification but the combination of MFCC, BPNN for the same feature set has not been proposed yet. To show the effectiveness of the work, the same process has been repeated with Support Vector Machine and the accuracy would be measured in both the cases.

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