Neural Network Training via Confidence-Based Experience Replay
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Solution Overview
Problem
Existing training methods for acoustic models based on neural networks are inefficient in terms of learning speed and training accuracy, particularly in speech recognition applications.
Innovation Solution
The proposed training method employs an experience replay mechanism, where samples with low confidence are selected more frequently for training, and well-recognized samples are gradually eliminated, enhancing the learning process through dynamic quota adjustments and domain-specific knowledge integration.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional training methods are used for acoustic models based on neural networks, then the training process is simple to implement, but the learning speed and training accuracy are insufficient
Solution Approach 1:
The patent implements dynamic quota adjustment for training samples based on confidence levels. The quota function dynamically changes the frequency of sample selection during training iterations, transitioning from static uniform sampling to adaptive sampling that emphasizes difficult samples while reducing redundancy from easy samples.
Solution Approach 2:
The patent introduces feedback mechanisms where the model's confidence output is used to adjust training sample selection. The confidence level from each training iteration feeds back into the quota function, which then determines the sampling frequency for the next iteration, creating a closed-loop training process that continuously adapts to model performance.
2Productivity
If all training samples are used with equal frequency, then the training process is straightforward, but the learning efficiency decreases due to redundant training on well-recognized samples
Solution Approach 1:
The patent applies partial action by selectively training on subsets of samples based on confidence levels. Instead of uniformly processing all samples, the system focuses training capacity on samples that provide the most learning value (low confidence samples), while reducing or eliminating redundant processing of well-recognized samples (high confidence samples).
Solution Approach 2:
The patent changes the sampling parameter (frequency) based on sample confidence levels. The quota function modifies the original uniform sampling distribution by assigning different weights to different samples, transforming the sampling strategy from a static equal-probability approach to a dynamic confidence-based approach.
3Loss of time
If the training set includes all samples throughout training, then comprehensive coverage is achieved, but training time increases due to unnecessary processing of mastered samples
Solution Approach 1:
The patent implements discarding of high-confidence samples from the training set as training progresses. Samples that achieve high confidence levels are gradually eliminated from the training pool, freeing up training time. The system recovers by reintroducing discarded samples only if their confidence degrades or if training requires review of previously mastered material.
Data Source
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AI summary
A training method and apparatus for speech recognition is disclosed, where an example of the training method includes determining whether a current iteration for training a neural network is performed by an experience replay iteration using an experience replay set, selecting a sample from at least one of the experience replay set and a training set based on a result of the determining, and training the neural network based on the selected sample.