Vehicle Speech Recognition Domain Segmentation
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Solution Overview
Problem
Current speech recognition systems face challenges in accuracy and user satisfaction due to high error rates in speech act estimation and domain-based recognition, leading to ambiguous interpretations and increased exception processes, especially as the number of domains and voice commands grows.
Innovation Solution
A method for managing user domains based on vehicle-mounted system information, generating customized conversations for speech recognition, and guiding users through optimized domain activation and exception processing, using a user domain model that reflects user preferences and selections to improve recognition accuracy and reliability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If domain-based speech recognition is used to improve recognition accuracy across multiple domains, then recognition coverage is improved, but processing speed decreases due to semantic analysis of all domains
Solution Approach 1:
The system segments domains into main domains and subdomains, and further divides them into active and inactive states. Only active domains undergo semantic analysis, while inactive domains are skipped. This segmentation allows the system to maintain comprehensive domain coverage while significantly reducing processing time by analyzing only relevant domains for each speech input.
2Adaptability or versatility
If the number of domains increases to support more services, then service versatility is improved, but user understanding of voice commands becomes difficult
Solution Approach 1:
The system applies local quality by providing customized conversation guidance specific to each user's active domains and preferences. Instead of presenting all possible domains, the system tailors guidance messages to the user's specific context, making the large number of supported services understandable and manageable for each individual user.
Solution Approach 2:
The system uses feedback from user interactions and exception processing to dynamically adjust domain activation and conversation guidance. When users encounter difficulties or ambiguities, the system learns from these exceptions and refines its guidance strategies, improving ease of operation over time while maintaining service versatility.
3Reliability
If exception processing is increased to handle ambiguous meanings, then recognition robustness is improved, but reliability deteriorates due to low accuracy in exceptional processes
Solution Approach 1:
The system performs preliminary actions by proactively providing conversation guidance before ambiguous interpretations occur. By guiding users with appropriate voice command examples based on their active domains and situation, the system prevents exceptions rather than merely handling them after they occur, thereby maintaining high accuracy while improving robustness.
Data Source
AI summary
A conversation guidance method of a speech recognition system may include managing a user domain based on speech recognition function information and situation information corrected from a system mounted on a vehicle, generating a conversation used for speech recognition based on the user domain, and guiding a user with the generated conversation.