Language modeling for conversational speech suffers from the limited amount of available adequate training data. This paper describes a new approach that performs the estimation of the language model probabilities in a continuous space, allowing by these means smooth interpolation of unobserved n-grams. This continuous space language model is used during the last decoding pass of a state-of-the-art conversational telephone speech recognizer to rescore word lattices. For this type of speech data, it achieves consistent word error reductions of more than 0.4% compared to a carefully tuned backoff n-gram language model.