1. A method of automated evaluation of a transcription quality, the method comprising: obtaining audio data; segmenting the audio data into a plurality of utterances with a voice activity detector operating on a computer processor; transcribing the plurality of utterances into at least one word lattice with a large vocabulary continuous speech recognition system operating on the processor, wherein each of the plurality of utterances is transcribed by the processor into a respective word lattice; applying, by the processor, a minimum Bayes risk decoder to the at least one word lattice to create at least one confusion network representing the at least one word lattice as a plurality sequential word bins and ε-bins, wherein the processor creates a confusion network for each word lattice; and calculating, with the processor, at least one conformity ratio from the least one confusion network, wherein the processor calculates a conformity ratio for each confusion network; calculating, with the processor, a transcription quality score from the at least one conformity ratio; filtering, by the processor, the plurality confusion networks based upon the calculated transcription plurality score of each confusion network; selecting, by the processor, those confusion networks from the plurality of confusion networks having a transcription quality score greater than a predetermined value; and storing, by the processor, the selected confusion networks as a plurality of high quality transcriptions.
3. The method of claim 1 , wherein calculating the conformity ration for each confusion network further comprises: identifying a probability value of a most probable word arc in each word bin; and calculating a joint probability for each ε-bin and a preceding word bin; wherein the conformity ratio is an average of the calculated joint probabilities for the confusion network.