Speech Recognition Error Handling via Meta-Dialogue Clarification
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Solution Overview
Problem
Conventional speech recognition systems suffer from low accuracy due to acoustic and semantic errors, and existing methods fail to effectively handle repeated errors in spoken dialogue systems, leading to inefficiencies and user frustration.
Innovation Solution
A meta-dialogue system that includes a speech recognition unit, a recognition error determination unit, and a meta-dialogue generation unit, which extracts speech features, determines sentence confidence, examines semantic structures, and generates questions to users to clarify errors, thereby improving error handling and dialogue efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional speech recognition systems are used, then the system structure is simple, but the speech recognition accuracy is low due to acoustic and semantic errors
Solution Approach 1:
The system segments the speech recognition process into multiple independent modules: acoustic recognition unit, semantic analysis unit, confidence evaluation unit, and error handling unit. Each module performs a specific function and can be developed independently, allowing the complex recognition task to be divided into manageable parts that collectively improve accuracy without overwhelming system complexity.
Solution Approach 2:
The patent introduces a meta-dialogue generation unit as an intermediary between the speech recognition system and the user. This intermediary processes recognition results, evaluates confidence levels, and generates appropriate follow-up questions or clarifications, thereby improving overall recognition accuracy through structured interaction without requiring complete redesign of the entire system.
2Measurement precision
If the system asks questions to clarify ambiguous portions, then recognition accuracy improves, but the dialogue time and user burden increase
Solution Approach 1:
The system applies partial action by selectively questioning only those portions of speech that exhibit low confidence scores or semantic ambiguities, rather than questioning the entire utterance. The meta-dialogue generation unit identifies specific ambiguous segments and generates targeted clarification questions, thereby improving accuracy while minimizing the time and user effort required compared to comprehensive re-questioning.
Solution Approach 2:
The system implements feedback mechanisms where recognition results are evaluated against confidence thresholds and semantic consistency checks. When errors or ambiguities are detected, the system provides feedback in the form of clarification questions to the user, and uses the user's responses to refine subsequent recognition attempts, creating a closed-loop system that improves accuracy efficiently.
3Extent of automation
If internal rules are used to eliminate ambiguous regions, then the system operates autonomously, but the error rate remains high due to incomplete internal rules
Solution Approach 1:
The patent introduces a meta-dialogue generation unit as an intermediary between the speech recognition system and the user. This intermediary processes recognition results, evaluates confidence levels, and generates appropriate follow-up questions or clarifications, thereby improving overall recognition accuracy through structured interaction without requiring complete redesign of the entire system.
Solution Approach 2:
The system implements feedback mechanisms where recognition results are evaluated against confidence thresholds and semantic consistency checks. When errors or ambiguities are detected, the system provides feedback in the form of clarification questions to the user, and uses the user's responses to refine subsequent recognition attempts, creating a closed-loop system that improves accuracy efficiently.
4Reliability
If repeated questions are asked to handle recognition errors, then error handling capability improves, but user frustration and dialogue inefficiency increase
Solution Approach 1:
The system applies partial action by selectively questioning only those portions of speech that exhibit low confidence scores or semantic ambiguities, rather than questioning the entire utterance. The meta-dialogue generation unit identifies specific ambiguous segments and generates targeted clarification questions, thereby improving accuracy while minimizing the time and user effort required compared to comprehensive re-questioning.
Solution Approach 2:
The system employs dynamic error handling where the meta-dialogue generation unit adapts its questioning strategy based on the specific type of error detected, the confidence score of the recognition result, and the context of the dialogue. This dynamic approach allows the system to adjust its behavior in real-time, improving reliability while maintaining user convenience by avoiding rigid, repetitive questioning patterns.
Data Source
AI summary
To handle portions of a recognized sentence having an error, a user is questioned about contents associated with portions. According to a user's answer, a result is obtained. Speech recognition unit extracts a speech feature of a speech signal inputted from user and finds a phoneme nearest to the speech feature to recognize a word. Recognition error determination unit finds a sentence confidence based on a confidence of the recognized word, performs examination of a semantic structure of a recognized sentence, and determines whether or not an error exists in the recognized sentence which is subjected to speech recognition according to predetermined criterion based on both sentence confidence and result of examining semantic structure. Meta-dialogue generation unit generates a question asking user for additional information based on content of a portion where the error exists and a type of the error.