Abstract: Most language models treat speech as simply sequences of words, ignoring the fact that words are also events in time. This paper reports an initial exploration of how word probabilities vary with time-into-utterance, and proposes a method for using this information to improve a language model. This is done by computing the ratio of the probability of the word at a specific time to its overall unigram probability, and using this ratio to adjust the n-gram probability. On casual dialogs from Switchboard this method gave a modest reduction in perplexity.