1. A method performed by one or more computers, the method comprising: receiving, by the one or more computers, data indicating a candidate transcription for an utterance and a particular context for the utterance; obtaining, by the one or more computers, a maximum entropy language model that includes (i) scores for one or more n-gram features that each correspond to a respective n-gram and (ii) scores for one or more backoff features that each correspond to a set of n-grams for which there are no corresponding n-gram features in the maximum entropy language model; determining, by the one or more computers, based on the candidate transcription and the particular context, a feature value for (i) each of the one or more n-gram features of the maximum entropy language model and (ii) each of the one or more backoff features of the maximum entropy language model; inputting, by the one or more computers, the feature values for the n-gram features and the feature values for the backoff features to the maximum entropy language model; and receiving, by the one or more computers, from the maximum entropy language model, an output indicative of a likelihood of occurrence of the candidate transcription; selecting, by the one or more computers, based on the output of the maximum entropy language model, a transcription for the utterance from among a plurality of candidate transcriptions; and providing, by the one or more computers, the selected transcription to a client device.
1. A method performed by one or more computers, the method comprising: receiving, by the one or more computers, data indicating a candidate transcription for an utterance and a particular context for the utterance; obtaining, by the one or more computers, a maximum entropy language model that includes (i) scores for one or more n-gram features that each correspond to a respective n-gram and (ii) scores for one or more backoff features that each correspond to a set of n-grams for which there are no corresponding n-gram features in the maximum entropy language model; determining, by the one or more computers, based on the candidate transcription and the particular context, a feature value for (i) each of the one or more n-gram features of the maximum entropy language model and (ii) each of the one or more backoff features of the maximum entropy language model; inputting, by the one or more computers, the feature values for the n-gram features and the feature values for the backoff features to the maximum entropy language model; and receiving, by the one or more computers, from the maximum entropy language model, an output indicative of a likelihood of occurrence of the candidate transcription; selecting, by the one or more computers, based on the output of the maximum entropy language model, a transcription for the utterance from among a plurality of candidate transcriptions; and providing, by the one or more computers, the selected transcription to a client device. 4. The method of claim 1 , wherein the scores of the maximum entropy language model comprise, for each context in a set of different contexts that each comprise a different sequence of one or more words, scores for: multiple different n-gram features that each correspond to the occurrence of a respective word with the context, the respective words forming a set of words, and a backoff feature that corresponds to the occurrence, with the context, of any word that is not in the set of words.