Computational Learning in Adaptive Systems for Spoken Conversation
The overall goal of the CLASSiC project is to facilitate the rapid deployment of accurate and robust spoken dialogue systems that can learn from experience. The approach will be based on statistical learning methods with a unified treatment of uncertainty across the entire system (speech recognition, spoken language understanding, dialogue management, natural language generation, speech synthesis). It will result in a modular processing framework with an explicit representation of uncertainty connecting the various sources of uncertainty (understanding errors, ambiguity, etc) to the constraints to be exploited (task, dialogue, and user contexts). The architecture supports a layered hierarchy of supervised learning and reinforcement learning in order to facilitate mathematically principled optimisation and adaptation techniques. It will be developed in close cooperation with our industrial partner in order to ensure a practical deployment platform as well as a flexible research test-bed.
See the CLASSiC website
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