Dialog System Proper Name Recognition via Contextual Weighting
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Solution Overview
Problem
Conventional spoken dialog systems face challenges in accurately recognizing proper names, especially in high-stress environments, due to inadequate speech recognition accuracy and reliance on static name lists that do not utilize contextual information such as temporal, recency, and context effects.
Innovation Solution
The implementation of a dialog system that utilizes contextual information to improve proper name recognition by formulating indirect confirmation questions that do not repeat the names, using a combination of static and dynamic databases with weight values to refine name lists and incorporating user models to enhance recognition accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If direct confirmation methods are used to verify proper names, then recognition accuracy can be improved, but the system becomes cumbersome and repetitive
Solution Approach 1:
The patent extracts the confirmation function from direct name repetition and implements it through indirect questions about contextual attributes. Instead of repeating the proper name directly for confirmation, the system asks questions about the entity's properties (e.g., asking about the destination city's characteristics rather than repeating the city name), thereby achieving confirmation without the cumbersome repetition of direct methods.
Solution Approach 2:
The patent introduces contextual information and indirect questions as intermediaries between the system and user for name confirmation. Rather than directly confirming the proper name through repetition, the system uses contextual attributes and indirect questions as a mediator to verify name recognition, reducing repetitiveness while maintaining accuracy.
2Device complexity
If static name lists are used for recognition, then system complexity is reduced, but recognition accuracy deteriorates due to lack of contextual information
Solution Approach 1:
The patent transforms the static name list into a dynamic system by incorporating contextual information that changes based on the conversation flow. The system dynamically adjusts recognition based on temporal context, recency of mentions, and contextual effects, allowing the name recognition mechanism to adapt to the current dialog state while maintaining system manageability.
Solution Approach 2:
The patent performs preliminary processing of contextual information and builds weighted name lists in advance of the actual recognition task. By pre-processing contextual data and preparing dynamic name lists with weight values before recognition is needed, the system reduces the complexity of real-time decision-making while improving recognition accuracy through pre-computed contextual relevance.
3Speed
If conventional speech recognition is used without contextual information, then processing speed is maintained, but recognition accuracy for proper names deteriorates
Solution Approach 1:
The patent performs preliminary computation of contextual weights and prepares dynamic name lists in advance, so that during actual speech recognition, the system can quickly query pre-computed contextual information rather than calculating it in real-time. This preliminary action maintains processing speed while incorporating contextual information to improve proper name recognition accuracy.
Solution Approach 2:
The patent implements feedback mechanisms where contextual information from previous dialog turns is continuously fed back into the recognition process. The system uses recency and contextual effect feedback to adjust name recognition dynamically, allowing fast processing through efficient feedback loops that leverage previously processed contextual data without requiring lengthy re-analysis.
Data Source
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AI summary
Embodiments of a dialog system that utilizes contextual information to perform recognition of proper names are described. Unlike present name recognition methods on large name lists that generally focus strictly on the static aspect of the names, embodiments of the present system take into account of the temporal, recency and context effect when names are used, and formulates new questions to further constrain the search space or grammar for recognition of the past and current utterances.