Support vector machines for noise robust ASR

Using discriminative classifiers, such as Support Vector Machines (SVMs) in combination with, or as an alternative to, Hidden Markov Models (HMMs) has a number of advantages for difficult speech recognition tasks. For example, the models can make use of additional dependencies in the observation sequences than HMMs provided the appropriate form of kernel is used. However standard SVMs are binary classifiers, and speech is a multi-class problem. Furthermore, to train SVMs to distinguish word pairs requires that each word appears in the training data. This paper examines both of these limitations. Tree-based reduction approaches for multiclass classification are described, as well as some of the issues in applying them to dynamic data, such as speech. To address the training data issues, a simplified version of HMM-based synthesis can be used, which allows data for any word-pair to be generated. These approaches are evaluated on two noise corrupted digit sequence tasks: AURORA 2.0; and actual in-car collected data.