Industrial Technology Research Institute

Tung-Hui Chiang

Yi-Chung Lin

Huei-Ming Wang

A portable dialogue management system includes a dialogue manager and a hierarchical task description table. The hierarchical task description table has a plurality of base tables connected with a hierarchical structure. Each base table defines the strategy of a sub-dialogue and stores the dialogue states, a number of domain parameters, and a plurality of response actions corresponding to each dialogue state. The dialogue manager manages the dialogue state of a dialogue system, determines the dialogue state and executes the appropriate response action. Because the domain knowledge is defined in the hierarchical task description table and the dialogue manager is not dependent on the application domain, the dialogue management system is easily portable to different applications. A stack may also be used to push in or pop up a dialogue state so that dialogues of multiple purposes can be accomplished.

Apparatus and method for portable dialogue management using a hierarchial task description table

A system for adaptively generating a composite noisy speech model to process speech in, e.g., a nonstationary environment comprises a speech recognizer, a re-estimation circuit, a combiner circuit, a classifier circuit, and a discrimination circuit. In particular, the speech recognizer generates frames of current input utterances based on received speech data and determines which of the generated frames are aligned with noisy states to produce a current noise model. The re-estimation circuit re-estimates the produced current noise model by interpolating the number of frames in the current noise model with parameters from a previous noise model. The combiner circuit combines the parameters of the current noise model with model parameters of a corresponding current clean speech model to generate model parameters of a composite noisy speech model. The classifier circuit determines a discrimination function by generating a weighted PMC HMM model. The discrimination learning circuit determines a distance function by measuring the degree of mis-recognition based on the discrimination function, determines a loss function based on the distance function, which is approximately equal to the distance function, determines a risk function representing the mean value of the loss function, and generates a current discriminative noise model based in part on the risk function, such that the input utterances correspond more accurately with the predetermined model parameters of the composite noisy speech model.

Growing community of inventors

Taichung, Taiwan

Tung-Hui Chiang