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Volume & Issue no: Volume 6, Issue 4, July - August 2017

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Title:
Speech Emotion Detection Based On Optimistic – DNN (Deep Neural Network) Approach
Author Name:
Inderjeet Kaur, Dr. Rakesh Kumar & Palwinder Kaur
Abstract:
Abstract Inrecent years, artificial intelligence like HCI (Human Computer Interface) has become very hot issue. The additional speech emotion recognition capabilities, speech based interfaces could be created more human-centric. The natural languages do not share the same features and variation in the process of speech, sound of speech; the recognition of speech accuracy gets affected with respect to the consumer’s language. The main aim of this research is to study the design of intonation and stress for voice emotion in English and to study the influence of person on emotion of speech’s recognition and accuracy. Speech information corresponds to the several type of emotions as Happy, Sad, Surprise, Joy and Aggressive. It was collected from 32 samples which contain various audio files. The research paper implements a combined system for detection of speech emotion by feature extraction using Gamma Tone Cepstral and Coefficient (GTCC); Enhanced Genetic Algorithm (EGA) used to extract features based on pitch, energy, filtered data and frequency. Classification is used to detect emotion in by supervised approach which have K-mean clustering algorithm and DNN algorithms. The consequences of the study defined are reduced features set gave better performance results as compared to SVM and Hybrid SVM approach. Keywords: Speech emotion recognition, Feature extraction, Gamma tone cepstralcoefficient(GTCC), Enhanced genetic algorithm, K-mean clustering and DNN algorithm.
Cite this article:
Inderjeet Kaur, Dr. Rakesh Kumar & Palwinder Kaur , " Speech Emotion Detection Based On Optimistic – DNN (Deep Neural Network) Approach" , International Journal of Emerging Trends & Technology in Computer Science (IJETTCS) , Volume 6, Issue 4, July - August 2017 , pp. 150-156 , ISSN 2278-6856.
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International Journal of Emerging Trends & Technology in Computer Science (IJETTCS)
ISSN 2278-6856
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