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Support Vector Machines as Acoustic Models in Speech Recognition

Speech recognition is usually based on Hidden Markov Models (HMMs), which represent the temporal dynamics of speech very efficiently, and Gaussian mixture models, which do non-optimally the classification (acoustic modeling) of speech into single speech units (phonemes). In this paper we present an overview about the use of Support Vector Machines (SVMs) for the classification task by integrating SVMs in a HMM-based speech recognition system. SVMs are very appealing due to their association with statistical learning theory and have already shown good results in pattern recognition. In our hybrid SVM/HMM system we use SVMs as acoustic models in a HMM-based decoder. We train and test the hybrid system on the DARPA Resource Management (RM1) corpus, showing better performance than HMM-based decoder using Gaussian mixtures. To reduce the effort for training of the SVMs, we also use mixtures of SVMs which scales nearly linearly with respect to the number of training vectors making it easier to deal with the large amount of speech data.