5. A method of testing a speech recognition system, comprising: receiving a text containing at least one word; generating a pronunciation for the text with a pronunciation tool, including a plurality of phonemes, by first accessing a pronunciation data store to obtain phonemes indicating pronunciation of the at least one word, and if the pronunciation data store does not contain phonemes for the at least one word, then providing the received text to a text-to-speech synthesizer to obtain the phonemes indicating the pronunciation of the at least one word; generating a model for each of the phonemes of the pronunciation and selecting a Hidden Markov Model sequence of states for a Hidden Markov Model (HMM) representative of each of the generated models, the selected sequence of HMM states being a sequence that the speech recognizer is to choose as a best sequence during recognition of speech that includes the at least one word, wherein generating a model includes selecting a plurality of candidate HMMs for at least one of the generated models; generating a sequence of feature vectors for the pronunciation from the model, wherein at least one of the feature vectors is generated by selecting, for each state in the sequence of HMM states, from a feature vector data store, a feature vector that has a closest probability distribution match with a given mixture in a Markov state in one of the generated models, such that the selected feature vectors produce a best score for the text when the selected feature vectors are provided to the speech recognizer during recognition of the at least one word; providing the sequence of vectors to the speech recognition system; and outputting text from the speech recognition system, in response to the provided sequence of vectors, for testing evaluation.
6. The method of claim 5 wherein generating the model further comprises: generating a sequence of model units for the sequence of phonemes.