1. A method of using alternate recognition hypotheses to improve whole-dialog understanding accuracy, the method comprising: receiving an utterance as part of a user dialog; generating an N-best list of recognition hypotheses for the user dialog to yield a generated N-best list; selecting, via a processor, an underlying user intention based on a belief distribution across the generated N-best list and at least one contextually similar N-best list that is based on an analysis of confidence scores calculated by summing over all possible hidden states and all possible hidden user actions associated with the recognition hypotheses in the generated N-best list and the at least one contextually similar N-best list; and responding to the user based on the selected underlying user intention.
1. A method of using alternate recognition hypotheses to improve whole-dialog understanding accuracy, the method comprising: receiving an utterance as part of a user dialog; generating an N-best list of recognition hypotheses for the user dialog to yield a generated N-best list; selecting, via a processor, an underlying user intention based on a belief distribution across the generated N-best list and at least one contextually similar N-best list that is based on an analysis of confidence scores calculated by summing over all possible hidden states and all possible hidden user actions associated with the recognition hypotheses in the generated N-best list and the at least one contextually similar N-best list; and responding to the user based on the selected underlying user intention. 2. The method of claim 1 , the method further comprising assigning an individual confidence score to each recognition hypothesis in the generated N-best list and in the at least one contextually similar N-best list.