Dynamic Neural Network Selection for Speech Recognition
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Solution Overview
Problem
The challenge in speech-to-text recognition is determining the optimal size of neural networks for real-time performance, as larger networks may be too slow for older processors while smaller networks compromise accuracy, and existing methods require a conservative approach that may not utilize the best network for varying hardware and environmental conditions.
Innovation Solution
A device with an input unit, propagation unit, and select unit dynamically selects between multiple neural networks of different sizes based on propagation time and target recognition speed, allowing seamless switching to ensure optimal network usage throughout the recognition process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a larger neural network is used, then recognition accuracy is improved, but processing speed deteriorates and may be too slow for real-time performance on older processors
Solution Approach 1:
The system dynamically selects between multiple neural networks of different sizes based on real-time propagation time measurements and target recognition speed requirements. The select unit monitors propagation time and switches between neural networks adaptively, allowing the system to use larger networks when speed permits and smaller networks when real-time performance is critical, thus resolving the contradiction between accuracy and speed.
2Speed
If a smaller neural network is used, then processing speed is improved for real-time performance, but recognition accuracy deteriorates
Solution Approach 1:
The system employs multiple neural networks of different sizes and dynamically selects the appropriate network based on current performance requirements. When real-time speed is critical, smaller networks are selected; when accuracy can be prioritized, larger networks are used. This dynamic selection resolves the contradiction by allowing both small and large networks to be utilized in different contexts rather than being forced to choose one conservative size.
3Reliability
If a conservatively smaller neural network is selected to cater for worst-case scenarios, then processing speed is maintained, but recognition accuracy suffers in best-case scenarios
Solution Approach 1:
Instead of statically selecting a conservatively small neural network, the system maintains multiple networks of different sizes and dynamically selects among them based on measured propagation time and target recognition speed. This allows the system to achieve real-time performance guarantees when needed while utilizing larger, more accurate networks when conditions permit, thereby resolving the contradiction between reliability and accuracy.
Solution Approach 2:
The system changes the parameter of neural network size dynamically based on performance metrics. By monitoring propagation time and comparing it to target recognition speed, the system adjusts which neural network configuration is active, switching between different network sizes to optimize both reliability and accuracy under varying conditions.
4Adaptability or versatility
If multiple neural networks of different sizes are maintained, then adaptability to varying conditions is improved, but device complexity increases
Solution Approach 1:
The system segments the neural network functionality into multiple networks of different sizes, each optimized for specific performance requirements. Rather than one monolithic network, the system divides the recognition task across multiple networks that can be selectively activated, improving adaptability while managing complexity through modular organization.
Solution Approach 2:
Multiple neural networks are maintained that can serve different functional requirements - larger networks for high-accuracy applications and smaller networks for real-time performance. This multi-functionality allows a single system to adapt to varying hardware conditions and performance requirements without requiring separate dedicated systems for each scenario.
Data Source
AI summary
Data is input to one of a plurality of neural networks. Each of the plurality of neural networks is to be of a different size. A propagation time is determined for the inputted data. The propagation time relates to a time for the inputted data to propagate through one of the plurality of neural networks. One of the plurality of neural networks is selected based on the propagation time.