Dialogue System Semantic Annotation Training
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Solution Overview
Problem
Existing dialogue systems require individual adaptation of speech models for each application, leading to inefficiencies in processing and understanding user inputs, as they struggle to accurately interpret speech inputs without additional context or user information.
Innovation Solution
A computer-implemented method for automatically training a dialogue system using a trainable semantic model that receives and classifies speech inputs, incorporating user information to improve the accuracy of semantic annotations, allowing for the learning of new meanings and trustworthiness assessment through contextual and temporal correlations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If individual adaptation of speech models is performed for each application, then the speech recognition can be optimized for specific expressions and commands, but the complexity of training and model maintenance increases significantly
Solution Approach 1:
The patent implements a universal speech model that can be applied across multiple applications without requiring individual adaptation for each application. The system achieves this by processing speech inputs through a common speech-to-text conversion process and then analyzing the resulting text data uniformly, eliminating the need for separate trained models for different applications while maintaining recognition accuracy.
2Productivity
If speech inputs are processed without additional context or user information, then the processing speed is maintained, but the accuracy of interpreting user inputs decreases
Solution Approach 1:
The system performs preliminary processing by converting speech to text data before analysis, and maintains a database of previously analyzed text data and user information. This preliminary organization of data structures and pre-processing of speech inputs enables the system to quickly retrieve relevant context and user information during analysis, improving interpretation accuracy without significantly impacting processing speed.
3Productivity
If semantic annotations are generated automatically without user feedback, then the system operates efficiently, but the reliability of semantic understanding decreases when inputs are incomplete or misinterpreted
Solution Approach 1:
The system implements a feedback mechanism where users can provide corrections or confirmations of automatically generated semantic annotations. The patent stores both the original text data and user feedback in a database, allowing the system to learn from corrections and improve future annotation accuracy. This feedback loop maintains high efficiency while progressively improving reliability through accumulated learning from user interactions.
Data Source
AI summary
An adaptive dialogue system and also a computer-implemented method for semantic training of a dialogue system are disclosed. In this connection, semantic annotations are generated automatically on the basis of received speech inputs, the semantic annotations being intended for controlling instruments or for communication with a user. For this purpose, at least one speech input is received in the course of an interaction with a user. A sense content of the speech input is registered and appraised, by the speech input being classified on the basis of a trainable semantic model, in order to make a semantic annotation available for the speech input. Further user information connected with the speech input is taken into account if the registered sense content is appraised erroneously, incompletely and/or as untrustworthy. The sense content of the speech input is learned automatically on the basis of the additional user information.