Voice Emotion Recognition using CNN and Decision Tree
Navya Damodar1, Vani H Y2, Anusuya M A3
1Navya Damodar, Information Science and Engg, JSS Science and Technological University, Mysuru, India.
2Vani H Y, Information Science and Engg, JSS science and Technological University, Mysuru, India.
3Anusuya M A, Computer Science and Engg, JSS science and Technological University, Mysuru, India.
Manuscript received on September 16, 2019. | Revised Manuscript received on 24 September, 2019. | Manuscript published on October 10, 2019. | PP: 4245-4249 | Volume-8 Issue-12, October 2019. | Retrieval Number: L26981081219/2019©BEIESP | DOI: 10.35940/ijitee.L2698.1081219
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: 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.
Keywords: Emotion Recognition(ER), Convolution Neural NetworkCNN), Mel Frequency Co-efficient (MFCC), Decision Tree (DT).
Scope of the Article: Pattern Recognition