 is a leaf and if the node was marked NEW (7-12) as set forth in FIG. 7, a variable L is set equal to R and a probability value of X is set equal to one (8-24). If L is in Node Set a probability value X is calculated by multiplying X by 1-P[L] (8-30). If L is not in Node_Set, L is set equal to PARENT[L].
By way of example and with reference to FIG. 3, if L was node “10” and was therefore a leaf and in Node_Set, the parent array 5-1 of node “10” is “20”. If “20” is not in Node_Set its probability is not calculated (8-30).
For each marked node in Node-Set (8-26) the probability is calculated (8-30) and the parent node is identified (8-28) and the process steps 8-28 and 8-30 until the root node is reached, where L is equal to zero (8-32). The value 1-X is the probability value assigned to the completed path corresponding to leaf R (8-34). If the value 1-X exceeds some threshold value, which is a design choice, the path is accepted as possible complete path. The path is accepted and the value M, which is the path counter, is incremented by 1 (8-36). PATH[M] is assigned the value of R, which is the leaf value for the path. The probability for the path, PR[M], is assigned the value 1-X. PR[M] is given by:
as calculated at reference numeral 8-30, where the product Π is taken over all marked nodes L on the path form the leaf R to the root and P[L] is determined in reference 7-12.
The variable NEW[R] prevents the system from querying the user about the same leaf more than once if the user has not uttered the keyword more than once.
The number Npaths is assigned the value of M (8-40) and control is returned to the procedure shown in FIG. 7 (7-14).
As set forth above, recognized keywords of a spoken utterance can be mapped to the nodes of the tree graph (70), the system thereafter tests to determine if the keywords are mapped to nodes that can be “linked” by intermediate edges (shown as the line segments between nodes). If a path can be established by nodes linking edges between the root node (0) and a leaf node (3, 4, 5, 6, 9, 10,11, 15, 19) the system considers the spoken utterance formed by the keywords mapped to the nodes on the path to be the sentence intended by the speaker.
An ideal result for the method is to have keywords that can each be marked into the tree graph. If the nodes of the tree graph that are so marked form a complete path between one and only one leaf node and the root node, the system determines that the sentence formed by the keywords associated with the nodes on the path form a sentence that the user intended to speak.
If input keywords do not map onto the tree graph to form a complete path, the system solicits additional keywords from the user by way of a prompt. Additional keywords are processed in the same fashion until the system can reasonably determine the content of the spoken utterance.
In the event several leaf nodes are mapped into the tree, the system identifies other nodes and determines the path based on some method to be the most likely. The determination of the most likely path is based on probabilistic considerations discussed below. If there is no leaf node mapped, the system prompts the user for more input in an attempt to identify at least one leaf node.
By iterative querying, and using probability theory, the method seeks to obtain a sufficient number of keywords such that a string of keywords that has been pre-specified to form a sentence can be recognized by the system. If such a number of keywords can be obtained from the user, the system outputs a response that is appropriate to the detected sentence.
The method can be enhanced to include context sensitive query interpretation. Context from a previous query can be used to process subsequent queries. This may be done by storing the path that satisfied the last query and treating it as if all nodes from that particular path were marked. Nodes that were not previously marked on a path would be assigned static probability values based on past experience gathered by the input device while all other nodes would be EMS assigned the same dy