Utterance-Based Color Matching Using Generated Color Expressions
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Solution Overview
Problem
Existing speech recognition systems fail to accurately match user utterances for colors when the entity name is not included in predefined natural language expressions, leading to inconclusive responses or the inability to provide a matching color.
Innovation Solution
Generate additional natural language expressions for each color code and determine a color code most similar to the generated expressions to match the user's intent, even if the entity name is not present in predefined tables.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the system uses a pre-defined table with limited natural language expressions for color matching, then the device complexity is reduced and processing speed is improved, but the color matching accuracy deteriorates when user utterances contain colors not in the pre-defined table
Solution Approach 1:
The system pre-generates multiple natural language expressions for each color code and stores them in advance. When a user utterance is received, the system checks if the extracted color entity matches any of the pre-generated expressions. This preliminary preparation enables fast matching without complex real-time processing while expanding the coverage of recognizable color expressions beyond a simple pre-defined table.
Solution Approach 2:
The system dynamically generates color codes based on the similarity between user utterances and existing color expressions. When no exact match is found, the system calculates similarity metrics and generates the most appropriate color code dynamically. This dynamic approach allows the system to handle novel color descriptions while maintaining accuracy, resolving the contradiction between handling diverse color inputs and maintaining system simplicity.
2Reliability
If the system checks only pre-defined natural language expressions for color entity names, then the processing time is reduced and response speed is improved, but the reliability of color provision deteriorates when the color entity is not in the pre-defined table
Solution Approach 1:
The system pre-generates multiple natural language expressions for each color code and stores them in advance. When a user utterance is received, the system checks if the extracted color entity matches any of the pre-generated expressions. This preliminary preparation enables fast matching without complex real-time processing while expanding the coverage of recognizable color expressions beyond a simple pre-defined table.
Solution Approach 2:
The system introduces an intermediary similarity calculation mechanism between the user utterance and existing color expressions. When no exact match is found in the pre-generated expressions, the system calculates similarity metrics to find the closest matching color. This intermediary approach maintains reliability for novel color descriptions while avoiding time-consuming complex processing, as the similarity calculation uses predefined metrics and thresholds.
3Adaptability or versatility
If the system provides an exact match response for color entity names, then the precision of color selection is improved, but the adaptability to user intent deteriorates when the exact color is not available or user intent is ambiguous
Solution Approach 1:
The system dynamically generates color codes based on the similarity between user utterances and existing color expressions. When no exact match is found, the system calculates similarity metrics and generates the most appropriate color code dynamically. This dynamic approach allows the system to handle novel color descriptions while maintaining accuracy, resolving the contradiction between handling diverse color inputs and maintaining system simplicity.
Solution Approach 2:
The system provides feedback to the user when an exact color match is not found, presenting the most similar color options. This feedback mechanism allows the system to adapt to user intent by suggesting alternative colors based on similarity calculations, while maintaining precision by base
4Measurement precision
If the system generates multiple natural language expressions for each color code, then the coverage of color expressions is improved and color matching accuracy is enhanced, but the quantity of data to be processed increases
Solution Approach 1:
The system pre-generates multiple natural language expressions for each color code and stores them in advance. When a user utterance is received, the system checks if the extracted color entity matches any of the pre-generated expressions. This preliminary preparation enables fast matching without complex real-time processing while expanding the coverage of recognizable color expressions beyond a simple pre-defined table.
Data Source
AI summary
A method and device provide a color that matches an intent of a user's utterance. The method includes: generating one or more second natural language expressions, each corresponding to a color represented by each of first color codes, for one or more of the first color codes in a pre-created table; and generating a response based on a result of interpreting the user's utterance related to the color.