Instance-Based Learning Algorithms
Machine Learning
C4.5: programs for machine learning
C4.5: programs for machine learning
An Exact Probability Metric for Decision Tree Splitting and Stopping
Machine Learning
Affective computing
Feature subset selection by Bayesian network-based optimization
Artificial Intelligence
Feature Selection for Knowledge Discovery and Data Mining
Feature Selection for Knowledge Discovery and Data Mining
Machine Learning
A study of distance-based machine learning algorithms
A study of distance-based machine learning algorithms
Introduction: 'Emotion and brain: Understanding emotions and modelling their recognition'
Neural Networks - Special issue: Emotion and brain
2005 Special Issue: Beyond emotion archetypes: Databases for emotion modelling using neural networks
Neural Networks - Special issue: Emotion and brain
ACII'05 Proceedings of the First international conference on Affective Computing and Intelligent Interaction
TSD'07 Proceedings of the 10th international conference on Text, speech and dialogue
Validating a multilingual and multimodal affective database
UI-HCII'07 Proceedings of the 2nd international conference on Usability and internationalization
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The study of emotions in human-computer interaction is a growing research area Focusing on automatic emotion recognition, work is being performed in order to achieve good results particularly in speech and facial gesture recognition In this paper we present a study performed to analyze different Machine Learning techniques validity in automatic speech emotion recognition area Using a bilingual affective database, different speech parameters have been calculated for each audio recording Then, several Machine Learning techniques have been applied to evaluate their usefulness in speech emotion recognition In this particular case, techniques based on evolutive algorithms (EDA) have been used to select speech feature subsets that optimize automatic emotion recognition success rate Achieved experimental results show a representative increase in the abovementioned success rate.