Vehicle Dialogue System Domain Selection for Speech Recognition
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Solution Overview
Problem
Vehicles face challenges in providing user convenience due to increased feature complexity, leading to driver distraction and difficulty in utilizing vehicle features, especially with inexperienced users, where existing speech recognition technologies struggle with accuracy and efficiency.
Innovation Solution
A vehicle dialogue system that includes an input processor, natural language processor, storage, controller, and result processor to rapidly and accurately recognize user utterances by selecting relevant domains based on morpheme analysis, location, time, and user behavior, prioritizing domains such as event or destination-related information, and communicating with external servers when necessary.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If speech recognition technology is applied to provide user convenience functions, then user convenience is improved, but driver distraction increases and safe driving is hindered
Solution Approach 1:
The system automatically analyzes user utterances and determines relevant domains without requiring driver intervention. The controller autonomously selects domains based on morpheme analysis results, location information, time information, and search frequency, allowing the system to serve itself in determining the appropriate context for user requests.
Solution Approach 2:
The system pre-stores multiple domains in the storage unit and performs preliminary analysis of user utterances through morpheme analysis before executing commands. By preparing domain information in advance and analyzing utterances beforehand, the system reduces the time and complexity of real-time processing during driving.
2Adaptability or versatility
If the number of domains is increased to cover more user needs, then adaptability is improved, but domain selection complexity increases and recognition accuracy decreases
Solution Approach 1:
The system segments the domain selection process into distinct analytical components: morpheme analysis of user utterances, location-based filtering, time-based filtering, and search frequency analysis. Each component handles a specific aspect of domain determination, making the overall complex process manageable and accurate.
Solution Approach 2:
The system dynamically adjusts domain selection based on real-time conditions including user location, time of day, and historical search patterns. The controller adapts the relevant domains according to the current context rather than using a static selection method, improving both accuracy and adaptability.
3Productivity
If traditional domain selection methods are used, then system simplicity is maintained, but speech recognition accuracy and speed are insufficient
Solution Approach 1:
The system incorporates feedback mechanisms by analyzing search frequency of domains and using this information to improve future domain selections. The controller learns from user behavior patterns and adjusts domain prioritization based on historical data, continuously improving recognition accuracy and speed.
Solution Approach 2:
The system performs preliminary morpheme analysis on user utterances and pre-determines relevant domains before command execution. By analyzing the utterance structure and comparing it against pre-stored domain information in advance, the system speeds up the recognition process while maintaining high accuracy.
4Adaptability or versatility
If multiple domains are always considered, then adaptability is improved, but processing time increases and response speed decreases
Solution Approach 1:
The system extracts only the most relevant domains from the complete set of pre-stored domains based on morpheme analysis, location, time, and search frequency. By taking out and focusing on the subset of domains most likely to match the user's intent, the system reduces processing time while maintaining adaptability.
Solution Approach 2:
The system dynamically determines the number and type of domains to consider based on current context. Rather than always processing all domains, the controller adapts the domain set size according to the specificity of the user utterance and available contextual information, optimizing the balance between adaptability and processing speed.
Data Source
AI summary
A vehicle may include: an input processor to receive a speech of a user and convert the speech into a text-type utterance; a natural language processor that performs morpheme analysis on the text-type utterance, and identifies an intent of the user and selects a domain related to the text-type utterance based on a result of the morpheme analysis; a storage to store a plurality of domains; a controller to add a new domain to the plurality of domains based on a specific condition, and determine a final domain among the plurality of domains and the added new domain; and a result processor to generate a command based on the determined final domain.