1. A computer-implemented method for training acoustic models in an automatic speech recognition system through a selection of acoustic data comprising: training a first acoustic model in the automatic speech recognition system using a training-data corpus comprising a plurality of speech audio files and a respective plurality of transcriptions for the plurality of speech audio files; 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 training-data corpus; creating a first subset of the training-data 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 training-data corpus comprising audio files retained from the one or more audio files of the first subset of the training-data 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 using the second subset of the training-data corpus.
1. A computer-implemented method for training acoustic models in an automatic speech recognition system through a selection of acoustic data comprising: training a first acoustic model in the automatic speech recognition system using a training-data corpus comprising a plurality of speech audio files and a respective plurality of transcriptions for the plurality of speech audio files; 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 training-data corpus; creating a first subset of the training-data 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 training-data corpus comprising audio files retained from the one or more audio files of the first subset of the training-data 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 using the second subset of the training-data corpus. 13. The method of claim 1 , further comprising using a mathematical equation δ = ∑ r = 1 R ⁢ ⁢ β r R to obtain the global frame likelihood score δ, wherein β r is an average frame likelihood score and R is the total number of the plurality of speech audio files.