Abstract
In this work, we propose latent semantic rational kernels (LSRK) for topic spotting on 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. With the proposed LSRK, all available external knowledge and techniques can be flexibly integrated into a unified WFST based framework to boost the topic spotting performance. We present how to generalize the LSRK using tf-idf weighting, latent semantic analysis, WordNet and probabilistic topic models. To validate the proposed LSRK framework, we conduct the topic spotting experiments on two datasets, Switchboard and AT&T HMIHY0300 initial collection. The experimental results show that with the proposed LSRK we can achieve significant and consistent topic spotting performance gains over the n-gram rational kernels.
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