DocumentCode :
1899775
Title :
A comprehensive survey on features and methods for speech emotion detection
Author :
Alva, M. Yashaswi ; Nachamai, M. ; Paulose, Joy
Author_Institution :
Dept. of Comput. Sci., Christ Univ., Bangalore, India
fYear :
2015
fDate :
5-7 March 2015
Firstpage :
1
Lastpage :
6
Abstract :
Human computer interaction will be natural and effective when the interfaces are sensitive to human emotion or stress. Previous studies were mainly focused on facial emotion recognition but speech emotion detection is gaining importance due its wide range of applications. Speech emotion recognition still remains a challenging task in the field of affective computing as no defined standards exist for emotion classification. Speech signal carries large information related to the emotions conveyed by a person. Speech recognition system fails miserably if robust techniques are not implemented to address the variations in speech due to emotion. Emotion detection from speech has two main steps. They are feature extraction and classification. The goal of this paper is to give an overview on the types of corpus, features and classification techniques that are associated with speech emotion recognition.
Keywords :
emotion recognition; feature extraction; human computer interaction; speech recognition; emotion classification; facial emotion recognition; feature classification technique; feature extraction; human computer interaction; human emotion; speech emotion detection; speech recognition system; speech signal; Emotion recognition; Hidden Markov models; Mel frequency cepstral coefficient; Silicon; Speech; Speech recognition; Support vector machines; Emotion recognition; classification methods; speech corpus; speech features;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Electrical, Computer and Communication Technologies (ICECCT), 2015 IEEE International Conference on
Conference_Location :
Coimbatore
Print_ISBN :
978-1-4799-6084-2
Type :
conf
DOI :
10.1109/ICECCT.2015.7226047
Filename :
7226047
Link To Document :
بازگشت