Latent semantic rational kernels for topic spotting on spontaneous conversational speech

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 n-gram 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.