Open AccessVoice Emotion Recognition using CNN and Decision Tree
Author(s) -
Navya Damodar*,
H Y VANI,
M A Anusuya
Publication year - 2019
Publication title -
international journal of innovative technology and exploring engineering
Language(s) - English
Resource type - Journals
ISSN - 2278-3075
DOI - 10.35940/ijitee.l2698.1081219
Subject(s) - computer science , speech recognition , decision tree , classifier (uml) , mel frequency cepstrum , emotion recognition , artificial intelligence , pattern recognition (psychology) , decision tree learning , natural language processing , feature extraction
This paper presents the use of decision tree and CNN as classifier to classify the emotions from the English and Kannada audio data. The performance of CNN and DT are potential for various emotions. Comparative study of the classifiers using various parameters is presented. The performance of CNN has been identified as the best classifier for emotion recognition. Emotions are recognized with 72% and 63% accuracy using CNN and Decision Tree algorithms respectively. MFCC features are extracted from the audio signals and Model is trained, tested and evaluated accordingly by changing the parameters. Speech Emotion Recognition system is useful in psychiatric diagnosis, lie detection, call centre conversations, customer voice review, voice messages.