Modeling time series data of varying length is important in different domains. There are two paradigms for modeling the varying length sequential data. Tasks such as speech recognition need modeling the temporal dynamics and the correlations among the features. Hidden Markov models (HMM) are used for these tasks. In tasks such as speaker recognition, audio classification and speech emotion recognition, modeling the temporal dynamics is not critical. Gaussian mixture models (GMM) are commonly used for these tasks. Generative models such as HMMs and GMMs focus on estimating the density of the data and are not suitable for classifying the data of confusable classes. Discriminative classifiers such as support vector machines (SVM) are suitable for the fixed dimensional patterns. In this paper, we propose a hybrid framework where a generative front end is used for representing the varying length time series data and then a discriminative model is used for classification. A score based approach and a segment modeling based approach are proposed in this framework. Both the approaches are applied for speech emotion recognition. The performance is compared with that of an SVM classifier that uses different statistical features and also with that of the GMM classifiers that use maximum likelihood method and the variational Bayes method for parameter estimation. Both the proposed approaches outperform the methods used for comparison. ©2009 IEEE.