Abstract: In a system (200) where a user's input is received by a user interface (201), users are free to use available input modalities in any order and at any time. In order to ensure that all inputs are collected before inferring the user's intent, an multi-modal input fusion (MMIF) module (204) receives the user input and attempts to fill available MMI templates (contained within a database (206)) with the user's input. The MMIF module (204) will wait for further modality inputs if no MMI template is filled. However, if any MMI template within the database (206) is filled completely, the MMIF module (204) will generate a semantic representation of the user's input with the current collection of user inputs. Additionally, if after a predetermined time no MMIF template has been filled, the MMIF module (204) will generate a semantic representation of the current user's input and output this representation.

Abstract: A method and system (102) is provided for a multi-modal task-oriented dialog management implemented on a computer-based system. The system (102) maximizes the probability of successful task completion after a task is requested (302). Every task is associated with a task model comprising recipes, acts, parameters and modalities. Confidence measures are calculated at various levels for each task. The confidence measures represent the probability of success of the action depending on the user preferences. The most suitable recipe, act, parameter and modality are selected at runtime using the provided confidence measures (304) to maximize the probability of task completion. After each act, confidence measures are modified (312) depending on the user response, and the next suitable act is accordingly selected. Optionally, a post evaluation module (PEM) is provided for monitoring the user response and modifying the formulation for the confidence measures calculation.

Abstract: A method (200) is described for creating decision trees for processing a sampled signal indicative of speech. The method (200) includes providing model sub vectors (220) from partitioned statistical speech models of phones, the models comprising vectors of mean values and associated variance values. The method (200) then provides for statistically analyzing (230) the model sub vectors of mean values to provide projection vectors indicating directions of relative maximum variance between the sub vectors and thereafter the method provides for calculating projection values (240) of the projection vectors. A selecting potential threshold values (260) step is then applied, the potential threshold values being determined from analysis of a range of the projection values. Finally a step of creating the decision trees (270) is effected to provide a decision tree having decisions to divide the model sub vectors into groups, the groups being leaves of the tree.