9. A method of transforming acoustic utterances into text in a speech recognition device, the method comprising: receiving one or more acoustic utterances via an acoustic sensor configured of the speech recognition device; storing a set of one or more acoustic models having trained one or more confidence classifiers and one or more acceptance metrics defining at least one recognition acceptance condition; generating a first speech recognition confidence classifier score corresponding to the one or more received acoustic utterances and recognized text based on a first confidence classifier; generating a second speech recognition confidence classifier score corresponding to the one or more received acoustic utterances and the recognized text based on a second confidence classifier; mapping a distribution within an output range of the second confidence classifier to a distribution within an output range of the first confidence classifier, the mapped distribution including a mapped speech recognition confidence classifier score for the second confidence classifier that more accurately satisfies the recognition acceptance condition than a corresponding score from the first confidence classifier; and outputting a signal representing new recognized text for a newly-received acoustic utterance as accepted text responsive to a determination that a mapped speech recognition confidence classifier score of the second confidence classifier for the newly-received acoustic utterance satisfies the recognition acceptance condition.
11. The method of claim 9 wherein the histogram-mapping operation comprises: generating probability mass functions for confidence scores from the first and second confidence classifiers, generating a cumulative mass functions corresponding to the probability mass functions for confidence scores from the first and second confidence classifiers, respectively, and generating an acceptance criteria map in which the cumulative mass function for the second classifier for each confidence score in the acceptance criteria map equals the cumulative mass function for the first classifier for each confidence score within a preset resolution.