Machine Learning
Proceedings of the 1998 conference on Advances in neural information processing systems II
Decontamination of Training Samples for Supervised Pattern Recognition Methods
Proceedings of the Joint IAPR International Workshops on Advances in Pattern Recognition
Combining Pattern Classifiers: Methods and Algorithms
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Pruning Training Sets for Learning of Object Categories
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Image Processing, Analysis, and Machine Vision
Image Processing, Analysis, and Machine Vision
Efficient sampling of training set in large and noisy multimedia data
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)
Use of Line Spectral Frequencies for Emotion Recognition from Speech
ICPR '10 Proceedings of the 2010 20th International Conference on Pattern Recognition
Paralinguistics in speech and language-State-of-the-art and the challenge
Computer Speech and Language
Ten recent trends in computational paralinguistics
COST'11 Proceedings of the 2011 international conference on Cognitive Behavioural Systems
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Training datasets containing spontaneous emotional expressions are often imperfect due the ambiguities and difficulties of labeling such data by human observers. In this paper, we present a Random Sampling Consensus (RANSAC) based training approach for the problem of emotion recognition from spontaneous speech recordings. Our motivation is to insert a data cleaning process to the training phase of the Hidden Markov Models (HMMs) for the purpose of removing some suspicious instances of labels that may exist in the training dataset. Our experiments using HMMs with various number of states and Gaussian mixtures per state indicate that utilization of RANSAC in the training phase provides an improvement of up to 2.84% in the unweighted recall rates on the test set. . This improvement in the accuracy of the classifier is shown to be statistically significant using McNemar's test.