1. A method for processing a voice command using a statistical dialog model, comprising: determining the voice command using a speech recognition of an audio signal; determining a belief state of the voice command as a probability distribution over states of states variables organized in a hierarchy with a parent-child relationship of nodes representing the states variables, such that the belief state includes the hierarchy of the state variables defining joint probabilities of each state of each state variable to correspond to the voice command, wherein a probability of a state of a child node in the hierarchy is conditioned on a probability of a state of a corresponding parent node, such that an intention of the voice command is represented by the hierarchy of state variables, and wherein the states of the hierarchy of state variables are states of Partially Observable Markov Decision Process (POMDP); selecting a subset of states from multiple levels of the hierarchy with highest value of state variables; selecting a set of candidate actions corresponding to the subset of states; selecting the system action from the set of candidate actions by solving the POMDP; and executing the system action, wherein steps of the method are performed by a processor.
1. A method for processing a voice command using a statistical dialog model, comprising: determining the voice command using a speech recognition of an audio signal; determining a belief state of the voice command as a probability distribution over states of states variables organized in a hierarchy with a parent-child relationship of nodes representing the states variables, such that the belief state includes the hierarchy of the state variables defining joint probabilities of each state of each state variable to correspond to the voice command, wherein a probability of a state of a child node in the hierarchy is conditioned on a probability of a state of a corresponding parent node, such that an intention of the voice command is represented by the hierarchy of state variables, and wherein the states of the hierarchy of state variables are states of Partially Observable Markov Decision Process (POMDP); selecting a subset of states from multiple levels of the hierarchy with highest value of state variables; selecting a set of candidate actions corresponding to the subset of states; selecting the system action from the set of candidate actions by solving the POMDP; and executing the system action, wherein steps of the method are performed by a processor. 6. The method of claim 1 , further comprising: selecting N-best-scored child nodes from one or more levels of the hierarchy, wherein the N-best-scored child nodes have highest probabilities among child nodes of their level, wherein N is an integer; and determining the subset of the state variables according to the probabilities of the N-best scored child nodes.