Topic language models, mostly revolving around the discovery of "word-document" co-occurrence dependence, have attracted significant attention and shown good performance in a wide variety of speech recognition tasks over the years. In this paper, a new topic language model, named word vicinity model (WVM), is proposed to explore the co-occurrence relationship between words, as well as the long-span latent topical information for language model adaptation. A search history is modeled as a composite WVM model for predicting a decoded word. The underlying characteristics and different kinds of model structures are extensively investigated, while the performance of WVM is thoroughly analyzed and verified by comparison with a few existing topic language models. Moreover, we also present a new modeling approach to our recently proposed word topic model (WTM), and design an efficient way to simultaneously extract "word-document" and "word-word" co-occurrence characteristics through the sharing of the same set of latent topics. Experiments on broadcast news transcription seem to demonstrate the utility of the presented models.