16. A method of training a speech model, comprising: obtaining model parameters for the speech model, wherein the speech model comprises an acoustic model and wherein obtaining model parameters for the speech model comprises performing maximum likelihood training on training data to obtain an initial model with initial model parameters; processing a known speech input using the speech model with the model parameters to generate a process result, wherein processing a known speech input to generate a process result comprises performing speech recognition on acoustic data indicative of a known acoustic input, using the acoustic model with the model parameters, to generate a speech recognition result; calculating a distance between a true result and the process result, given the model parameters and the known speech input, the true result comprising a true transcription, the true transcription corresponding to only the following waveform states: silence, noise, onset and speech, instead of a phonetic transcription; modifying the model parameters to reduce the distance between the true result and the process result, to obtain a modified model wherein reducing the distance between the true result and the process result comprises maximizing a logarithmic function comprising a parameter set for the acoustic model and a super utterance, the super utterance comprising a feature vector sequence, the true result and the process result; and iterating on the steps of processing, calculating and modifying until the model parameters reach a desired convergence.
16. A method of training a speech model, comprising: obtaining model parameters for the speech model, wherein the speech model comprises an acoustic model and wherein obtaining model parameters for the speech model comprises performing maximum likelihood training on training data to obtain an initial model with initial model parameters; processing a known speech input using the speech model with the model parameters to generate a process result, wherein processing a known speech input to generate a process result comprises performing speech recognition on acoustic data indicative of a known acoustic input, using the acoustic model with the model parameters, to generate a speech recognition result; calculating a distance between a true result and the process result, given the model parameters and the known speech input, the true result comprising a true transcription, the true transcription corresponding to only the following waveform states: silence, noise, onset and speech, instead of a phonetic transcription; modifying the model parameters to reduce the distance between the true result and the process result, to obtain a modified model wherein reducing the distance between the true result and the process result comprises maximizing a logarithmic function comprising a parameter set for the acoustic model and a super utterance, the super utterance comprising a feature vector sequence, the true result and the process result; and iterating on the steps of processing, calculating and modifying until the model parameters reach a desired convergence. 17. The method of claim 16 wherein the true result comprises a true transcription of the acoustic data and wherein calculating a distance comprises: calculating a true transcription measure indicative of a probability of generating the true transcription, given the acoustic data and the model parameters; calculating a speech recognition measure indicative of a probability of generating the speech recognition result, given the acoustic data and the model parameters; and calculating the distance as the difference between the transcription measure and the speech recognition measure.