Robot Voice Interaction Using Semantic Recognition and Knowledge Base
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current robots lack high-degree interactive capabilities, failing to effectively simulate character interactions with interactive objects due to limited recognition and feedback mechanisms.
Innovation Solution
An interactive method for robots that involves obtaining voice information, performing semantic recognition to determine conversation intentions, retrieving feedback information from a pre-configured knowledge base, and converting it into voice feedback to simulate user interactions, enhancing interaction freedom and intelligence.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If robots use simple voice recognition and playback mechanisms, then device complexity is reduced, but interactive capability and character simulation are limited
Solution Approach 1:
The system segments the interaction process into distinct modules: voice information acquisition, semantic recognition, knowledge base retrieval, and voice playback. This modular segmentation allows each component to be optimized independently while maintaining overall system complexity at manageable levels.
Solution Approach 2:
A conversation scenario knowledge base acts as an intermediary between the simple voice recognition system and the desired complex character simulation. The knowledge base pre-stores conversation patterns, character traits, and response templates, enabling the robot to simulate characters without requiring complex real-time decision-making algorithms.
2Measurement precision
If robots implement comprehensive semantic recognition and knowledge base retrieval, then conversation understanding improves, but processing time increases
Solution Approach 1:
The conversation scenario knowledge base is pre-configured with character information, conversation patterns, and response templates before actual interaction begins. This preliminary preparation allows the system to retrieve pre-processed information during interaction, significantly reducing real-time processing time while maintaining high recognition accuracy.
3Extent of automation
If robots use pre-configured knowledge bases, then interaction intelligence improves, but system adaptability to new scenarios decreases
Solution Approach 1:
The knowledge base is designed with a universal structure that can store multiple character profiles, conversation scenarios, and interaction patterns. This universal design allows the same system framework to handle diverse interaction scenarios by simply loading different knowledge base configurations, maintaining both intelligence and flexibility.
Data Source
AI summary
Embodiments of the present disclosure provide an interactive method of a robot, an interactive device of a robot and a device. The method includes: obtaining voice information input by an interactive object, and performing semantic recognition on the voice information to obtain a conversation intention; obtaining feedback information corresponding to the conversation intention based on a conversation scenario knowledge base pre-configured by a simulated user; and converting the feedback information into a voice of the simulated user, and playing the voice to the interactive object.