8. A method of training a speech recognition learning system either offline or online, the method comprising: generating at least one text to speech output to an utterance using a user simulator during the training, the output having qualities comprising at least one of a voice, a pitch, a rate or a volume; identifying speech features related to the utterance using a speech model or using a dialog action model; identifying the utterance that is in need of clarification by initiating a repair dialog, wherein the utterance associated with the repair dialog is identified includes: determining what states of the repair dialog are reached from other states, the dialog system learns which states to go to when observing an appropriate speech and dialog features by trying all repair paths using the user simulator where a user's voice is generated using various text-to-speech (TTS) engines at adjustable levels; and determining which states of the repair dialog are failures or successes; performing an action related to the speech features; and updating the dialog action model, the speech model or both, based at least in part on the speech features, the action, or a combination thereof.
8. A method of training a speech recognition learning system either offline or online, the method comprising: generating at least one text to speech output to an utterance using a user simulator during the training, the output having qualities comprising at least one of a voice, a pitch, a rate or a volume; identifying speech features related to the utterance using a speech model or using a dialog action model; identifying the utterance that is in need of clarification by initiating a repair dialog, wherein the utterance associated with the repair dialog is identified includes: determining what states of the repair dialog are reached from other states, the dialog system learns which states to go to when observing an appropriate speech and dialog features by trying all repair paths using the user simulator where a user's voice is generated using various text-to-speech (TTS) engines at adjustable levels; and determining which states of the repair dialog are failures or successes; performing an action related to the speech features; and updating the dialog action model, the speech model or both, based at least in part on the speech features, the action, or a combination thereof. 10. The method of claim 8 , wherein the dialog action model further comprises a probability distribution associated with uncertainty regarding the dialog action model parameters or the speech model parameters.