Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Machine Learning
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Affect and Emotion in Human-Computer Interaction
Emotion Recognition through Multiple Modalities: Face, Body Gesture, Speech
Affect and Emotion in Human-Computer Interaction
HCI'07 Proceedings of the 12th international conference on Human-computer interaction: intelligent multimodal interaction environments
Computer Speech and Language
An extraction of emotion in human speech using cluster analysis and a regression tree
ACS'10 Proceedings of the 10th WSEAS international conference on Applied computer science
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The goal of this research was to develop a system that will automatically measure changes in the emotional state of a speaker, by analyzing his/her voice. Natural (non-acted) human speech of 77 (Dutch) speakers was collected and manually splitted into speech units. Three recordings per speaker were collected, in which he/she was in a positive, neutral and negative state. For each recording, the speakers rated 16 emotional states on a 10-point Likert Scale. The Random Forest algorithm was applied to 207 speech features that were extracted from recordings to qualify (classification) and quantify (regression) the changes in speaker's emotional state. Results showed that predicting the direction of change of emotions and the change of intensity, measured by Mean Squared Error, can be done better than the baseline (the mean value of change). Moreover, it turned out that changes in negative emotions are more predictable than changes in positive emotions.