1. A method for reducing the transcription effort for training an automatic speech recognition module, the method comprising: (1) training acoustic and language models using a first set of transcribed data; (2) recognizing utterances in a set of candidates for transcription using the acoustic and language models; (3) computing by a processor confidence scores of the utterances; (4) selecting k utterances that have the smallest confidence scores from the set of candidates and transcribing them into a first additional transcribed set; (5) adding the first additional transcribed set to the first set of transcribed data to produce a second set of transcribed data; (6) removing the first additional transcribed set from the set of candidates; (7) retrieving a set of un-transcribed data from the set of candidates; (8) training the acoustic and language models using the second set of transcribed data and speech recognition and word confidence scores for the retrieved set of un-transcribed data; and (9) returning to step (1) if word accuracy has not converged.
1. A method for reducing the transcription effort for training an automatic speech recognition module, the method comprising: (1) training acoustic and language models using a first set of transcribed data; (2) recognizing utterances in a set of candidates for transcription using the acoustic and language models; (3) computing by a processor confidence scores of the utterances; (4) selecting k utterances that have the smallest confidence scores from the set of candidates and transcribing them into a first additional transcribed set; (5) adding the first additional transcribed set to the first set of transcribed data to produce a second set of transcribed data; (6) removing the first additional transcribed set from the set of candidates; (7) retrieving a set of un-transcribed data from the set of candidates; (8) training the acoustic and language models using the second set of transcribed data and speech recognition and word confidence scores for the retrieved set of un-transcribed data; and (9) returning to step (1) if word accuracy has not converged. 6. The method of claim 1 , wherein step (7) comprises selecting a sample of un-transcribed data.