  This paper presents a novel method of generating and applying hierarchical,
dynamic topic-based language models. It proposes and evaluates new cluster
generation, hierarchical smoothing and adaptive topic-probability estimation
techniques. These combined models help capture long-distance lexical
dependencies. Experiments on the Broadcast News corpus show significant
improvement in perplexity (10.5% overall and 33.5% on target vocabulary).
