1. A method for use in accordance with a dialog system, the method comprising: generating at least one language model, the at least one language model being conditioned on a state of dialog associated with the dialog system; and storing the at least one language model for subsequent use in accordance with a speech recognizer associated with the dialog system; wherein generating the at least one language model conditioned on a state of dialog associated with the dialog system further comprises: dividing training data which is labeled by state into different state sets depending on the state to which the training data belongs; clustering the state sets into clustered state sets, the clustering comprising combining training data belonging to states that are close to each other based on a distance measure; building a separate language model for each of the clustered state sets to create a plurality of separate language models; and building at least one interpolated model by interpolating one or more of the plurality of separate language models with a base model obtained from training data that includes at least some training data used to train all of the plurality of separate language models.
1. A method for use in accordance with a dialog system, the method comprising: generating at least one language model, the at least one language model being conditioned on a state of dialog associated with the dialog system; and storing the at least one language model for subsequent use in accordance with a speech recognizer associated with the dialog system; wherein generating the at least one language model conditioned on a state of dialog associated with the dialog system further comprises: dividing training data which is labeled by state into different state sets depending on the state to which the training data belongs; clustering the state sets into clustered state sets, the clustering comprising combining training data belonging to states that are close to each other based on a distance measure; building a separate language model for each of the clustered state sets to create a plurality of separate language models; and building at least one interpolated model by interpolating one or more of the plurality of separate language models with a base model obtained from training data that includes at least some training data used to train all of the plurality of separate language models. 5. The method of claim 1 , wherein each separate language model is a trigram language model.