International Journal of Advance Computational Engineering and Networking (IJACEN)
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Statistics report
Dec. 2019
Submitted Papers : 80
Accepted Papers : 10
Rejected Papers : 70
Acc. Perc : 12%
Issue Published : 80
Paper Published : 1200
No. of Authors : 3014
  Journal Paper

Paper Title
Speech Emotion Recognition Using Iterative Clustering Technique

Abstract
This paper proposes a method to recognize the emotion present in the speech signal using Iterative clustering technique. We propose Mel Frequency Perceptual Linear Predictive Cepstrum (MFPLPC) as a feature for recognizing the emotions. This feature is extracted from the speech and the clustering models are generated for each emotion. For the Speaker Independent classification technique, pre- processing is done on test speeches and features are extracted. In K- means clustering algorithm, the classification is based on the minimum distance between each test vector and centroid of clusters. Mean of the minimum distances for each speech is found out. The test speech belongs to the model which has minimum of averages corresponds to particular emotion. We obtained better recognition rate by using MFPLPC feature for a cluster size 1024. The results are obtained for SAVEE database using data of seven emotional states such as anger, disgust, fear, happy, sad, surprise and neutral. KeyWords- Emotion recognition (ER), Mel Frequency Perceptual Linear Predictive Cepstrum (MFPLPC), Vector Quantization (VQ).


Author - R.anila, Dr. A. Revathy

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| Published on 2015-06-17
   
   
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