Dynamic Vocabulary Weight Adjustment for Speech Recognition
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Solution Overview
Problem
Intelligent electronic devices struggle to determine frequently used functions based on user behavior, as users have varying habits and preferences, leading to inefficiencies in recognizing operational patterns.
Innovation Solution
A recognition network generation device and method that includes an operation record storage, activity model constructor, activity predictor, and weight adjustor to classify operation records, select relevant activity models, and adjust recognition vocabulary weights based on device peripheral information, enhancing speech recognition accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the electronic device uses a fixed speech recognition vocabulary, then the device complexity is reduced, but the speech recognition accuracy deteriorates due to inability to adapt to user behavior patterns
Solution Approach 1:
The patent implements dynamic adjustment of recognition vocabulary weights based on operation records. The weight adjustor continuously updates the importance of different vocabularies according to actual user behavior patterns, transforming the static recognition system into a dynamic one that adapts to changing usage habits while maintaining manageable complexity through automated learning
Solution Approach 2:
The system performs self-learning by automatically analyzing operation records and adjusting vocabulary weights without external intervention. The weight adjustor autonomously identifies frequently used functions and modifies recognition priorities based on accumulated usage data, enabling the device to improve its own performance through self-service mechanisms
2Measurement precision
If the electronic device analyzes all operation records to determine user preferences, then the speech recognition accuracy improves, but the loss of time increases due to extensive data processing
Solution Approach 1:
The system performs preliminary classification of operation records into activity models before detailed analysis. By pre-organizing data into structured activity patterns during idle periods, the system reduces the processing burden during actual speech recognition tasks, enabling quick matching of current operations against pre-analyzed behavior patterns
Solution Approach 2:
The activity predictor selects only the most relevant activity models for comparison with current device peripheral information, rather than analyzing all possible activity patterns. This partial action approach focuses computational resources on the most probable user behaviors, reducing overall processing time while maintaining recognition accuracy
3Adaptability or versatility
If the recognition vocabulary weights are adjusted frequently, then the adaptability to user behavior improves, but the device complexity increases due to continuous weight adjustment operations
Solution Approach 1:
The system implements feedback mechanisms where operation records continuously inform weight adjustments. The weight adjustor monitors usage patterns and automatically adjusts vocabulary weights based on feedback from actual device operations, creating a closed-loop system that adapts to user behavior while maintaining stable operation through systematic update rules
Solution Approach 2:
The patent changes the parameter being optimized from complete vocabulary re-ranking to selective weight adjustment of individual vocabularies. By modifying only the weight parameters of frequently used terms rather than restructuring the entire recognition network, the system achieves adaptability with minimal complexity increase
Data Source
AI summary
A recognition network generation device, disposed in an electronic device, comprising: an operation record storage device storing a plurality of operation records of the electronic device, wherein each of the operation records includes operation content executed by the electronic device and device peripheral information detected by the electronic device when the electronic device executes the operation content; an activity model constructor classifying the operation records into a plurality of activity models according to all the device peripheral information of the operation records; an activity predictor selecting at least one selected activity model according to the degree of similarity between each of the activity models and a current device peripheral information detected by the electronic device; and a weight adjustor adjusting the weights of a plurality of recognition vocabularies by taking into account a number of times each recognition vocabulary appears in all operations contents of the activity models, wherein the recognition vocabularies correspond to all the operation content of the at least one selected activity model.