10. A computer-implemented method for automatic speech recognition that extracts words from user speech, the system comprising: classifying, via a processor, a set of labeled candidate utterances based on a previously trained classifier; generating, via a processor, classification types for each candidate utterance; receiving confidence scores for the classification types from the trained classifier; sorting the classified utterances based on an analysis of the confidence score of each candidate utterance compared to a respective label of the candidate utterance, wherein the analysis is based at least in part on a Kullback-Liebler divergence; and rechecking candidate utterances when the Kullback-Liebler divergence is greater than a threshold.
10. A computer-implemented method for automatic speech recognition that extracts words from user speech, the system comprising: classifying, via a processor, a set of labeled candidate utterances based on a previously trained classifier; generating, via a processor, classification types for each candidate utterance; receiving confidence scores for the classification types from the trained classifier; sorting the classified utterances based on an analysis of the confidence score of each candidate utterance compared to a respective label of the candidate utterance, wherein the analysis is based at least in part on a Kullback-Liebler divergence; and rechecking candidate utterances when the Kullback-Liebler divergence is greater than a threshold. 11. The computer-implemented method of claim 10 , wherein the analysis is based on a comparison of the generated classification type of the candidate utterance and the label of the candidate utterance, and the module rechecks the candidate utterances when the generated classification type does not match the label of the candidate utterance.