Abstract:We propose an extension to the recent approaches in topic-mixture modeling such as Latent Dirichlet Allocation and Topic Tracking Model for the purpose of unsupervised adaptation in speech recognition. Instead of using the 1-best input given by the speech recognizer, the proposed model takes confusion network as an input to alleviate recognition errors. We incorporate a selection variable which helps reweight the recognition output, thus creating a more accurate latent topic estimate. Compared to adapting base… Show more
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