Personalized Voice Model Learning in Electronic Devices
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Solution Overview
Problem
Current voice recognition systems lack personalization, leading to inconsistent user experiences as they do not effectively adapt to individual user inputs and preferences.
Innovation Solution
An electronic device with a processor and memory that selects user input data, analyzes it, extracts additional information, learns a personalized voice model, and provides response data using this model, allowing for tailored interactions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a voice recognition system uses general voice models, then the system can serve multiple users, but the user experience becomes inconsistent and lacks personalization
Solution Approach 1:
The system performs preliminary learning of user-specific voice characteristics by analyzing initial voice inputs and extracting features. This preliminary action creates a personalized voice model before the actual voice recognition task, enabling consistent user experience while maintaining system versatility across multiple users.
Solution Approach 2:
The system incorporates feedback mechanisms where voice recognition results are fed back into the learning module. This feedback loop continuously refines the personalized voice model by comparing actual user inputs with recognized outputs, improving accuracy and consistency of user experience over time without requiring complex manual configuration.
2Productivity
If the system processes only the data directly from user input, then the processing is simple, but additional useful information may be lost
Solution Approach 1:
The system extracts not only the primary voice recognition data but also additional related information such as usage context, temporal patterns, and semantic meanings. This extraction process enriches the original data with useful information that enhances voice recognition accuracy while maintaining a relatively simple processing pipeline through automated feature extraction.
Solution Approach 2:
The system performs preliminary analysis and extraction of additional information features before the main voice recognition processing. By pre-extracting and organizing additional data such as contextual metadata and semantic representations, the system improves overall information extraction efficiency without adding significant complexity to the core recognition pipeline.
Data Source
AI summary
Disclosed is an electronic device. The electronic device includes: a processor, and a memory operatively connected to the processor, the memory stores instructions that, when executed, cause the processor to: select at least one data received through a user input, analyze the selected data, extract additional data based on the analyzed data, learn a personal voice model using the data and the additional data, and provide response data using the personal voice model.