1. A method of training an automatic speech recognition module, the method comprising: training, via a processor of a computing device, acoustic and language models using a set of transcribed data, speech recognition scores, and word confidence scores for a retrieved set of un-transcribed data, wherein the set of transcribed data, speech recognition scores, and word confidence scores is generated by steps comprising: recognizing utterances in a set of candidates for transcription using acoustic and language models trained using an initial set of transcribed data; computing confidence scores of the utterances; selecting a subset of utterances that have the smallest confidence scores from the set of candidates and transcribing them into a transcribed set; adding the transcribed set to the initial set of transcribed data to produce an updated set of transcribed data, speech recognition scores, and word confidence scores; selecting a pre-determined amount of un-transcribed data, the un-transcribed data being associated with utterances not selected in the selecting of the subset of utterances; and applying the pre-determined amount of un-transcribed data to train the acoustic and language models; and iteratively performing the training if word accuracy has not converged.