Dialogue System Context-Aware Intent Matching
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Solution Overview
Problem
Dialogue systems face inaccuracies in speech recognition and user intent understanding due to STT engine misrecognition and ambiguity, leading to decreased trust and satisfaction in services provided.
Innovation Solution
A dialogue system that includes a Speech to Text engine, a learning-based dialogue engine, and a controller to determine actual user intentions using context information and additional user speech, allowing for learning and matching of spoken text with actual user intentions when initial intent determination fails.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If STT engine performs speech recognition conversion, then user speech is converted to text, but misrecognition and ambiguity occur causing decreased accuracy
Solution Approach 1:
The system implements feedback mechanisms where the NLU engine provides feedback on intent recognition results to the STT engine. When misrecognition is detected, the system uses feedback loops to correct and refine speech-to-text conversion, continuously improving accuracy through iterative refinement of recognition results
Solution Approach 2:
The system performs preliminary actions by pre-processing speech signals and text data before final recognition. Context information is prepared and stored in advance, allowing the system to make more accurate intent determinations when processing user speech, thereby improving overall recognition accuracy
2Measurement precision
If NLU engine determines user intention from spoken text, then intent is identified, but fails when speech is ambiguous or misrecognized
Solution Approach 1:
The NLU engine is designed with multi-functionality to handle various speech patterns and ambiguities. It can process not only clear direct commands but also ambiguous, partial, or context-dependent speech inputs, making the system adaptable to diverse user communication styles and situations
Solution Approach 2:
Context information serves as an intermediary element between the spoken text and intent determination. When speech is ambiguous, the system uses context information as a mediator to bridge the gap, allowing the NLU engine to infer correct intent by combining textual analysis with contextual clues from previous interactions and user behavior patterns
3Device complexity
If dialogue system uses only spoken text for intent determination, then processing is simple, but accuracy decreases due to STT errors
Solution Approach 1:
The system merges multiple data sources including spoken text, context information, and user behavior data into a unified processing approach. By combining these different types of information, the system achieves higher intent recognition accuracy without excessive complexity, as the merged data is processed through an integrated NLU engine that handles all input types cohesively
Data Source
AI summary
A dialogue system includes a Speech to Text (STT) engine configured to convert a user speech into a spoken text; a learning-based dialogue engine configured to determine a user intention corresponding to the spoken text; a storage configured to store learning data used for learning of the dialogue engine; and a controller configured to determine an actual user intention based on at least one of context information or an additional user speech, match a spoken text failing to determine the user intention with the actual user intention when the dialogue engine fails to determine the user intention corresponding to the spoken text, and the dialogue engine may perform learning using the spoken text stored in the storage and the actual user intention.