Fundamentals of speech recognition
Fundamentals of speech recognition
Combining labeled and unlabeled data with co-training
COLT' 98 Proceedings of the eleventh annual conference on Computational learning theory
Classification of general audio data for content-based retrieval
Pattern Recognition Letters - Special issue on image/video indexing and retrieval
Active learning: theory and applications
Active learning: theory and applications
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Learning with online constraints: shifting concepts and active learning
Learning with online constraints: shifting concepts and active learning
Learning from labeled features using generalized expectation criteria
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
ECML '07 Proceedings of the 18th European conference on Machine Learning
A Novel Feature for Emotion Recognition in Voice Based Applications
ACII '07 Proceedings of the 2nd international conference on Affective Computing and Intelligent Interaction
Curious machines: active learning with structured instances
Curious machines: active learning with structured instances
Similarity-based clustering of sequences using hidden Markov models
MLDM'03 Proceedings of the 3rd international conference on Machine learning and data mining in pattern recognition
Fuzzy-input fuzzy-output one-against-all support vector machines
KES'07/WIRN'07 Proceedings of the 11th international conference, KES 2007 and XVII Italian workshop on neural networks conference on Knowledge-based intelligent information and engineering systems: Part III
On instance selection in audio based emotion recognition
ANNPR'12 Proceedings of the 5th INNS IAPR TC 3 GIRPR conference on Artificial Neural Networks in Pattern Recognition
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Automatic emotion classification is a task that has been subject of study from very different approaches. Previous research proves that similar performance to humans can be achieved by adequate combination of modalities and features. Nevertheless, large amounts of training data seem necessary to reach a similar level of accurate automatic classification. The labelling of training, validation and test sets is generally a difficult and time consuming task that restricts the experiments. Therefore, in this work we aim at studying self and active training methods and their performance in the task of emotion classification from speech data to reduce annotation costs. The results are compared, using confusion matrices, with the human perception capabilities and supervised training experiments, yielding similar accuracies.