6. The method of claim 1 , wherein the miscue is identified with one or more miscue categories and the user-perceptible feedback is based in part on one of the miscue categories with which the miscue in the acoustic input is identified, wherein the miscue categories comprise one or more of: word repetition, breath, partial word, pause, hesitation or elongation, wrong word, mispronunciation, background noise, interjection or insertion, non-speech sound, and hyperarticulation.
1. A computer-implemented method comprising: providing a user option to tune a weighting parameter, and responding to tuning of the weighting parameter by raising or lowering criteria for a general-domain garbage language model to adjust a miscue detection rate and a rate of false alarms; displaying a text output having target words; dynamically generating a domain-specific target language model for the text output, the target language model being specific to the text output having the target words and including a language score for the target words of the text output; receiving an acoustic input; modeling, using a processor of a computer, the acoustic input with the dynamically generated domain-specific target language model, comprising calculating an acoustic score for the target words with reference to the acoustic input; further modeling the acoustic input with the general-domain garbage language model to identify an element of the acoustic input as a miscue that does not correspond properly to the target words of the text output; and providing user-perceptible feedback.