Speaker identification and verification using Gaussian mixture speaker models
Speech Communication
The nature of statistical learning theory
The nature of statistical learning theory
Combining labeled and unlabeled data with co-training
COLT' 98 Proceedings of the eleventh annual conference on Computational learning theory
Data mining: practical machine learning tools and techniques with Java implementations
Data mining: practical machine learning tools and techniques with Java implementations
Improved Boosting Algorithms Using Confidence-rated Predictions
Machine Learning - The Eleventh Annual Conference on computational Learning Theory
Analyzing the effectiveness and applicability of co-training
Proceedings of the ninth international conference on Information and knowledge management
Baby ears: a recognition system for affective vocalizations
Speech Communication
Enhancing Supervised Learning with Unlabeled Data
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Selective Sampling with Redundant Views
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
Efficient co-regularised least squares regression
ICML '06 Proceedings of the 23rd international conference on Machine learning
Automatic discrimination between laughter and speech
Speech Communication
Multiple-view multiple-learner active learning
Pattern Recognition
Coupled semi-supervised learning
Coupled semi-supervised learning
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This paper describes the development of an infant-directed speech discrimination system for parent-infant interaction analysis. Different feature sets for emotion recognition were investigated using two classification techniques: supervised and semi-supervised. The classification experiments were carried out with short pre-segmented adult-directed speech and infant-directed speech segments extracted from real-life family home movies (with durations typically between 0.5s and 4s). The experimental results show that in the case of supervised learning, spectral features play a major role in the infant-directed speech discrimination. However, a major difficulty of using natural corpora is that the annotation process is time-consuming, and the expression of emotion is much more complex than in acted speech. Furthermore, interlabeler agreement and annotation label confidences are important issues to address. To overcome these problems, we propose a new semi-supervised approach based on the standard co-training algorithm exploiting labelled and unlabelled data. It offers a framework to take advantage of supervised classifiers trained by different features. The proposed dynamic weighted co-training approach combines various features and classifiers usually used in emotion recognition in order to learn from different views. Our experiments demonstrate the validity and effectiveness of this method for a real-life corpus such as home movies.