Word topical mixture models for dynamic language model adaptation

This paper considers dynamic language model adaptation for Mandarin broadcast news recognition. A word topical mixture model (TMM) is proposed to explore the co-occurrence relationship between words, as well as the long-span latent topical information, for language model adaptation. The search history is modeled as a composite word TMM model for predicting the decoded word. The underlying characteristics and different kinds of model structures were extensively investigated, while the performance of word TMM was analyzed and verified by comparison with the conventional probabilistic latent semantic analysis-based language model (PLSALM) and trigger-based language model (TBLM) adaptation approaches. The large vocabulary continuous speech recognition (LVCSR) experiments were conducted on the Mandarin broadcast news collected in Taiwan. Very promising results in perplexity as well as character error rate reductions were initially obtained.