Voice Interaction System Using Dynamic Learning Model Selection
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Solution Overview
Problem
Existing voice interaction systems face challenges in preventing response errors due to inappropriate learning models, leading to issues like speech collisions and long silences, which affect the accuracy and effectiveness of user interactions.
Innovation Solution
A voice interaction system that includes a speech acquisition unit, feature extraction unit, response determination unit, response execution unit, response error determination unit, and learning model selection unit, which allows for the selection of an appropriate learning model based on response errors to improve response accuracy and prevent errors such as speech collisions and long silences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If one machine learning model is used to determine responses, then the system structure is simple, but response errors occur due to inappropriateness of the learning model for different user states
Solution Approach 1:
The patent segments the single learning model into multiple learning models, each trained on different types of data (e.g., emotional state data, speech content data, contextual data). The system selects the appropriate learning model based on the current user state, thereby improving response accuracy without significantly increasing overall system complexity through modular design.
Solution Approach 2:
The patent implements dynamic selection of learning models based on real-time user state assessment. The system dynamically determines which learning model is most appropriate for the current situation (e.g., selecting emotional state-based model when user is excited, or context-based model during informational exchange), allowing the system to adapt to changing user states and prevent response errors.
2Ease of operation
If a single learning model is used, then the system is easy to operate, but speech collisions and long silences occur due to inappropriate responses
Solution Approach 1:
The patent incorporates feedback mechanisms where the system monitors response outcomes (e.g., detecting speech collisions or long silences) and uses this feedback to select more appropriate learning models for subsequent responses. The system learns from interaction patterns and adjusts learning model selection accordingly, improving interaction quality while maintaining ease of operation through automated feedback-driven adaptation.
3Reliability
If multiple learning models are used to improve response accuracy, then response errors are reduced, but the system complexity increases
Solution Approach 1:
The patent performs preliminary classification of user states and pre-selects the most appropriate learning model before generating responses. By assessing user state characteristics in advance and choosing the corresponding learning model proactively, the system improves response accuracy while avoiding the complexity of simultaneously maintaining and managing multiple competing response generation paths.
Data Source
AI summary
A voice interaction system capable of appropriately handling a situation to effectively prevent a response error from occurring. A speech acquisition unit 102 acquires user speech. A feature extraction unit 104 extracts a feature of the acquired user speech. A response determination unit 120 determines a response corresponding to the extracted feature using any one of a plurality of learning models. A response execution unit 130 performs control in order to execute the determined response. A response error determination unit 140 determines whether the executed response is an error. A learning model selection unit 150 selects the learning model from the plurality of learning models stored in a learning model database 160 according to a result of the determination by the response error determination unit 140. The response determination unit 120 determines the response using the selected learning model.