Improved spoken term detection using support vector machines with acoustic and context features from pseudo-relevance feedback

This paper reports a new approach to improving spoken term detection that uses support vector machine (SVM) with acoustic and linguistic features. As SVM is a good technique for discriminating different features in vector space, we recently proposed to use pseudo-relevance feedback to automatically generate training data for SVM training and use SVM to re-rank the first-pass results considering the context consistency in the lattices. In this paper, we further extend this concept by considering acoustic features at word, phone and HMM state levels and linguistic features of different order. Extensive experiments under various recognition environments demonstrate significant improvements in all cases. In particular, the acoustic features at the HMM state level offered the most significant improvements, and the improvements achieved by acoustic and linguistic features are shown to be additive.