To search, Click below search items. |
| All Published Papers Search Service |
|
|
|
Title |
Speech Emotion Recognition with SVM, KNN and DSVM |
|
Author |
Hadhami Aouani and Yassine Ben Ayed |
| Citation |
Vol. 23 No. 8 pp. 40-48 |
|
Abstract |
Speech Emotions recognition has become the active research theme in speech processing and in applications based on human-machine interaction. In this work, our system is a two-stage approach, namely feature extraction and classification engine. Firstly, two sets of feature are investigated which are: the first one is extracting only 13 Mel-frequency Cepstral Coefficient (MFCC) from emotional speech samples and the second one is applying features fusions between the three features: Zero Crossing Rate (ZCR), Teager Energy Operator (TEO), and Harmonic to Noise Rate (HNR) and MFCC features. Secondly, we use two types of classification techniques which are: the Support Vector Machines (SVM) and the k-Nearest Neighbor (k-NN) to show the performance between them. Besides that, we investigate the importance of the recent advances in machine learning including the deep kernel learning. A large set of experiments are conducted on Surrey Audio-Visual Expressed Emotion (SAVEE) dataset for seven emotions. The results of our experiments showed given good accuracy compared with the previous studies. |
|
Keywords |
Emotion recognition, MFCC, ZCR, TEO, HNR, KNN, SVM, Deep SVM. |
|
URL |
