7. The computer-implemented method of claim 1 , wherein determining the probability that a predefined command is associated with the speech data comprises: calculating a probability that each sparse vector associated with a predefined command includes the term frequency vector associated with the speech data using a unigram model.
1. A computer-implemented method for classifying speech data including one or more words, the method comprising the steps of: storing a plurality of predefined commands, each predefined command including one or more words and each predefined command associated with an action; receiving one or more alternate formats associated with each predefined command from a data source, each alternate format including one or more words and an identifier associating the alternate format with a predefined command; for each predefined command, representing the predefined command with a sparse vector comprising the term frequency-inverse document frequency (“TFIDF”) weights for each word of the predefined command and the one or more alternate formats associated with the predefined command; receiving the speech data; generating a term frequency vector associated with the speech data; determining, for each predefined command, the probability that the speech data is associated with the predefined command based on the sparse vector associated with each predefined command and the term frequency vector associated with the speech data; associating the speech data with a predefined command based on the determined probabilities; and executing the action associated with the predefined command associated with the speech data.