In this work, we propose latent semantic rational kernels (LSRK) for topic spotting on spontaneous conversational speech. Rather than mapping the input weighted finite-state transducers (WFSTs) onto a high dimensional n-gram feature space as in ngram rational kernels, the proposed LSRK maps the WFSTs onto a latent semantic space. Moreover, with the LSRK framework, all available external knowledge can be flexibly incorporated to boost the topic spotting performance. The experiments we conducted on a spontaneous conversational task, Switchboard, show that our method can achieve significant performance gain over the baselines from 27.33% to 57.56% accuracy and almost double the classification accuracy over the n-gram rational kernels in all cases.
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