Structured Support Vector Machines for Noise Robust Continuous Speech Recognition

The use of discriminative models is an interesting alternative to generative models for speech recognition. This paper examines one form of these models, structured support vector machines (SVMs), for noise robust speech recognition. One important aspect of structured SVMs is the form of the joint feature space. In this work features based on generative models are used, which allows model-based compensation schemes to be applied to yield robust joint features. However, these features require the segmentation of frames into words, or sub-words, to be specified. In previous work this segmentation was obtained using generative models. Here the segmentations are refined using the parameters of the structured SVM. A Viterbi-like scheme for obtaining "optimal" segmentations, and modifications to the training algorithm to allow them to be efficiently used, are described. The performance of the approach is evaluated on a noise corrupted continuous digit task: AURORA 2.