Author(s): K. J. Patil, P. H. Zope, S. R. Suralkar
Published in: International Journal of Engineering Research & Technology
License: This work is licensed under a Creative Commons Attribution 4.0 International License.
Volume/Issue: Vol.1 - Issue 9 (November - 2012)
In these years, literature about automatic emotion recognition is growing dramatically due to the development of techniques in computer vision, speech analysis and machine learning. However, automatic recognition on emotions occurring on natural communication setting is a largely unexplored and challenging problem. Speech processing is emerged as one of the important application area of digital signal processing. Various fields for research in speech processing are emotion detection from speech, speech recognition, speaker recognition, speech synthesis, speech coding etc. The objective of automatic emotion detection is to extract, characterize and recognize the information of speaker°∆s emotions. Feature extraction is the first step for speaker recognition. Many algorithms are suggested/developed by the researchers for feature extraction. In this report, the Mel Frequency Cepstrum Coefficient (MFCC) feature has been used for designing an automatic emotion detection system. Some modifications to the existing technique of MFCC for feature extraction are also suggested to improve the emotion detection efficiency. This report presents an approach to emotion recognition from speech signals. In this report, the framework to extract features from the speech signal that can be used for the detection of emotional state of the speaker. An essential step in the generation of expressive speech synthesis is the automatic detection and classification of emotions most likely to be present in speech input.
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