Word Topic Models for Spoken Document Retrieval and Transcription

Abstract

Statistical language modeling (LM), which aims to capture the regularities in human natural language and quantify the acceptability of a given word sequence, has long been an interesting yet challenging research topic in the speech and language processing community. It also has been introduced to information retrieval (IR) problems, and provided an effective and theoretically attractive probabilistic framework for building IR systems. In this article, we propose a word topic model (WTM) to explore the co-occurrence relationship between words, as well as the long-span latent topical information, for language modeling in spoken document retrieval and transcription. The document or the search history as a whole is modeled as a composite WTM model for generating a newly observed word. The underlying characteristics and different kinds of model structures are extensively investigated, while the performance of WTM is thoroughly analyzed and verified by comparison with the well-known probabilistic latent semantic analysis (PLSA) model as well as the other models. The IR experiments are performed on the TDT Chinese collections (TDT-2 and TDT-3), while the large vocabulary continuous speech recognition (LVCSR) experiments are conducted on the Mandarin broadcast news collected in Taiwan. Experimental results seem to indicate that WTM is a promising alternative to the existing models.