Classification of Multi-variate Varying Length Time Series Using Descriptive Statistical Features
PReMI '09 Proceedings of the 3rd International Conference on Pattern Recognition and Machine Intelligence
Combination of generative models and SVM based classifier for speech emotion recognition
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Variational conditional random fields for online speaker detection and tracking
Speech Communication
International Journal of Speech Technology
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In this paper applicability of variational methods for estimation of parameters of models used for speech emotion recognition is discussed.When the amount of data available is not adequate for training complex models, variational Bayesian method helps in training models with less amount of data. It also helps in determining the optimal complexity of the model. Our studies on Berlin emotional speech database show that variational methods perform better than maximum likelihood approach to estimate parameters of Gaussian mixture models used in speech emotion recognition.