Instance-Based Learning Algorithms
Machine Learning
C4.5: programs for machine learning
C4.5: programs for machine learning
Steps toward artificial intelligence
Computers & thought
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
Data Mining using MLC++, A Machine Learning Library in C++
ICTAI '96 Proceedings of the 8th International Conference on Tools with Artificial Intelligence
A study of distance-based machine learning algorithms
A study of distance-based machine learning algorithms
A computational model for the automatic recognition of affect in speech
A computational model for the automatic recognition of affect in speech
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
Validating a multilingual and multimodal affective database
UI-HCII'07 Proceedings of the 2nd international conference on Usability and internationalization
ACII'05 Proceedings of the First international conference on Affective Computing and Intelligent Interaction
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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, including techniques based on evolutive algorithms (EDA) to select speech feature subsets that optimize automatic emotion recognition success rate. Achieved experimental results show a representative increase in the success rate.