Topic Dependent Language Model based on Topic Voting on Noun History

摘要

Language models (LMs) are important in automatic speech recognition systems. In this paper, we propose a new approach to a topic dependent LM, where the topic is decided in an un-supervised manner. Latent Semantic Analysis (LSA) is employed to reveal hidden (latent) relations among nouns in the context words. To decide the topic of an event, a fixed size word history sequence (window) is observed, and voting is then carried out based on noun class occurrences weighted by a confidence measure. Experiments on the Wall Street Journal corpus and Mainichi Shimbun (Japanese newspaper) corpus show that our proposed method gives better perplexity than the comparative baselines, including a word-based/class-based n-gram LM, their interpolated LM, a cache-based LM, and the Latent Dirichlet Allocation (LDA)-based topic dependent LM.