15. A computer-implemented method for training acoustic models in an automatic speech recognition system comprising: training a first acoustic model in the automatic speech recognition system using a speech corpus comprising a plurality of speech audio files and a respective plurality of transcriptions for the plurality of speech audio files by calculating a maximum likelihood criterion of the speech corpus and estimating parameters of a probability distribution of the first acoustic model that maximize the maximum likelihood criterion; performing a forced Viterbi alignment of the plurality of speech audio files using the trained first acoustic model in the automatic speech recognition system; calculating a global frame likelihood score δ for the plurality of speech audio files, wherein the global frame likelihood score δ comprises an average of frame likelihoods over the speech corpus; creating a first subset of the speech corpus comprising one or more audio files by selecting the one or more audio files from the plurality of speech audio files based on the global frame likelihood score δ; performing a phoneme recognition of the plurality of speech audio files using the trained first acoustic model and the respective plurality of transcriptions in the automatic speech recognition system; calculating a global phoneme recognition accuracy ν for the plurality of speech audio files; creating a second subset of the speech corpus comprising audio files retained from the one or more audio files of the first subset of the speech corpus which meet at least one predetermined criterion indicating that an audio file has good audio quality; and training a second acoustic model in the automatic speech recognition system with said second subset of the speech corpus.
25. The method of claim 15 , wherein creating the subset speech corpus comprises automatically rejecting bad quality files and transcriptions from the speech corpus.