A computer-implemented method for training a transcription model, the method comprising: for each of a set of utterances: inputting an utterance that comprises a set of spectrogram frames into a first layer of the transcription model that evaluates each of the spectrogram frames from the set of spectrogram frames with a context of one or more spectrogram frames; outputting from the transcription model a predicted character or character probabilities for the utterance; and computing a loss to measure error in prediction for the utterance; evaluating a gradient of predicted outputs of the transcription model given the ground-truth characters; and updating the transcription model using back-propagation; characterized in that the method further comprises: jittering at least some of the set of utterances prior to inputting into the first layer of the transcription model, comprising: generating a jitter set of utterances for an utterance by translating an audio file of the utterance by one or more time values, the one or more time values are half a filter bank step size; converting the jitter set of utterances and the utterance into a set of spectrograms; obtaining output results from the transcription model or from a set of transcription models for the set of spectrograms; and blending the output results for the set of spectrograms to obtain an output for the audio file. for each of a set of utterances: inputting an utterance that comprises a set of spectrogram frames into a first layer of the transcription model that evaluates each of the spectrogram frames from the set of spectrogram frames with a context of one or more spectrogram frames; outputting from the transcription model a predicted character or character probabilities for the utterance; and computing a loss to measure error in prediction for the utterance; inputting an utterance that comprises a set of spectrogram frames into a first layer of the transcription model that evaluates each of the spectrogram frames from the set of spectrogram frames with a context of one or more spectrogram frames; outputting from the transcription model a predicted character or character probabilities for the utterance; and computing a loss to measure error in prediction for the utterance; evaluating a gradient of predicted outputs of the transcription model given the ground-truth characters; and updating the transcription model using back-propagation; characterized in that the method further comprises: jittering at least some of the set of utterances prior to inputting into the first layer of the transcription model, comprising: generating a jitter set of utterances for an utterance by translating an audio file of the utterance by one or more time values, the one or more time values are half a filter bank step size; converting the jitter set of utterances and the utterance into a set of spectrograms; obtaining output results from the transcription model or from a set of transcription models for the set of spectrograms; and blending the output results for the set of spectrograms to obtain an output for the audio file. generating a jitter set of utterances for an utterance by translating an audio file of the utterance by one or more time values, the one or more time values are half a filter bank step size; converting the jitter set of utterances and the utterance into a set of spectrograms; obtaining output results from the transcription model or from a set of transcription models for the set of spectrograms; and blending the output results for the set of spectrograms to obtain an output for the audio file. <EOS>