Adaptive Speech Recognition Depth Using Hidden Layer Accuracy Prediction
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Solution Overview
Problem
Existing speech recognition models perform iterative calculations through all hidden layers regardless of speech difficulty, leading to low efficiency for speeches with relatively low recognition difficulty and poor accuracy for those with high difficulty.
Innovation Solution
A speech recognition method that dynamically adjusts the number of hidden layers participating in calculations based on the predicted accuracy of each layer, allowing for early output when sufficient accuracy is achieved and additional layers when needed, using an accuracy prediction model to determine the necessity of further iterations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If all hidden layers are used for every speech recognition task, then speech recognition accuracy is maintained, but speech recognition efficiency deteriorates
Solution Approach 1:
The patent implements dynamic adjustment of the number of hidden layers based on speech difficulty assessment. The system evaluates each speech input and adaptively selects the appropriate number of hidden layers to engage, transforming the static architecture into a dynamic one that optimizes computational resources while maintaining recognition accuracy for varying speech complexities
Solution Approach 2:
Different hidden layers are assigned different functional roles based on speech difficulty. Simpler speeches utilize only the necessary subset of hidden layers for accurate recognition, while more complex speeches engage additional layers. This local differentiation allows the system to apply computational resources precisely where needed rather than uniformly across all inputs
2Productivity
If fewer hidden layers are used, then speech recognition efficiency is improved, but speech recognition accuracy deteriorates
Solution Approach 1:
The system dynamically determines the optimal number of hidden layers based on real-time speech difficulty assessment. By evaluating speech characteristics before processing, the system can flexibly adjust the computational depth to match the actual recognition needs, ensuring sufficient accuracy without unnecessary computational overhead
Solution Approach 2:
The patent changes the parameter of hidden layer quantity based on speech difficulty metrics. Instead of using a fixed number of hidden layers, the system adjusts this parameter dynamically according to the complexity of the input speech, thereby optimizing the balance between efficiency and accuracy for different recognition scenarios
Data Source
AI summary
A speech recognition method includes: inputting a speech into a speech recognition model which includes N hidden layers; predicting speech recognition accuracy corresponding to the ith hidden layer according to the hidden layer information and an accuracy prediction model that are corresponding to the ith hidden layer, the speech recognition accuracy indicating a degree of matching between a speech recognition result determined according to the hidden layer information of the ith hidden layer and an accurate speech recognition result corresponding to the speech; outputting the hidden layer information corresponding to the ith hidden layer to an (i+1)th hidden layer in response to that the speech recognition accuracy does not meet the result output condition; and outputting the hidden layer information corresponding to the ith hidden layer to the output layer in response to that the speech recognition accuracy meets a result output condition, to obtain the speech recognition result.