Speech Recognition Model Adaptation Threshold Control
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Solution Overview
Problem
Speech recognition systems face inefficiencies in adapting their models, leading to potential degradation in performance and wastage of computational, storage, and power resources, especially in battery-powered mobile devices, due to inefficient adaptation processes.
Innovation Solution
Implementing a method to control model adaptation in speech recognition systems using recognition rate thresholds and desired confidence levels, which allows for adaptive learning and resource optimization by determining accurate recognition rates and adjusting models based on these criteria, thereby preventing undesirable adaptations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If model adaptation is performed continuously to improve recognition accuracy, then recognition accuracy is improved, but computational resources, storage space, and power consumption increase excessively
Solution Approach 1:
The system implements feedback mechanisms by monitoring recognition accuracy metrics and using this information to control when model adaptation should occur. The feedback loop compares current recognition performance against thresholds and adjusts adaptation frequency accordingly, preventing unnecessary adaptations that would consume additional power while ensuring adaptations occur when they will improve accuracy.
Solution Approach 2:
The system changes operational parameters by introducing confidence levels and recognition rate thresholds as control variables. These parameters dynamically adjust the adaptation process, allowing the system to operate in different modes (adaptive vs. static) based on current performance needs, thereby optimizing the balance between accuracy improvement and power consumption.
2Reliability
If model adaptation is performed frequently to improve recognition accuracy, then recognition accuracy is improved, but computational resources are wasted on unnecessary adaptations
Solution Approach 1:
The system uses feedback from recognition rate measurements to control the adaptation process. By continuously monitoring whether recognition accuracy has actually improved after adaptations, the system can detect when further adaptations are unnecessary and stop the adaptation process, preventing waste of computational resources on redundant operations.
Solution Approach 2:
The system performs self-regulation by automatically determining when adaptation is needed based on its own performance metrics. The speech recognition system monitors its own recognition accuracy and autonomously controls the adaptation process without external intervention, enabling it to stop adaptations when performance plateaus and avoid unnecessary computational expenditure.
3Reliability
If model adaptation is performed to improve recognition accuracy, then recognition accuracy is improved, but storage space is consumed for storing adapted models
Solution Approach 1:
The system introduces confidence levels as a parameter to control model adaptation. By requiring that adaptations only occur when confidence in the improvement exceeds a certain threshold, the system reduces the number of adaptations performed, thereby reducing the accumulation of adapted models that would consume storage space while still achieving necessary accuracy improvements.
4Productivity
If recognition rate thresholds and confidence levels are used to control model adaptation, then resource optimization is achieved, but system complexity increases
Solution Approach 1:
The system applies multi-functionality by using the same confidence level and threshold parameters across different adaptation scenarios and model types. These universal control parameters manage diverse adaptation processes (acoustic model adaptation, language model adaptation, etc.) through a unified framework, reducing the need for separate control mechanisms for each adaptation type and thereby limiting the increase in system complexity.
Data Source
AI summary
A method for efficient use of resources of a speech recognition system includes determining a recognition rate, corresponding to either recognition of instances of a word or recognition of instances of various words among a set of words, and determining an accuracy range of the recognition rate. The method may further include adjusting adaptation of a model for the word or various models for the various words, based on a comparison of at least one value in the accuracy range with a recognition rate threshold. An apparatus for efficient use of resources of a speech recognition system includes a processor adapted to determine a recognition rate corresponding to either recognition of instances of a one word or recognition of instances of various words among a set of words, and an accuracy range of the recognition rate. The apparatus may further include a controller adapted to adjust adaptation of a model for the word or various models for the various words, based on a comparison of at least one value in the accuracy range with a recognition rate threshold.