Variations in rate of speech (ROS) produce changes in both spectral features and word pronunciations that affect automatic speech recognition (ASR) systems. To deal with these ROS effects, we propose to use parallel, rate-specific, acoustic models: one for fast speech, the other for slow speech. Rate switching is permitted at word boundaries, to allow modeling within-sentence speech rate variation, which is common in conversational speech. Due to the parallel structure of ratespecific models and the maximum likelihood decoding method, we do not need high-quality ROS estimation before recognition, which is usually hard to achieve. In this paper, we evaluate our approach on a large-vocabulary conversational speech recognition (LVCSR) task over the telephone, with several minimal pair comparisons based on different baseline systems.