Conservative DNN Adaptation via KL Regularization
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Solution Overview
Problem
Conventional automatic speech recognition (ASR) systems, particularly those using deep neural networks (DNNs), face challenges in adapting to individual users or contexts due to the difficulty in obtaining sufficient training data and the complexity of model parameters, leading to suboptimal recognition performance.
Innovation Solution
The implementation of a context-dependent deep neural network hidden Markov model (CD-DNN-HMM) system that adapts DNN parameters for a particular user or context using a conservative approach, such as Kullback-Leibler Divergence regularization, to constrain deviations in output distributions and prevent overfitting, allowing for improved recognition capabilities without requiring extensive user-specific training data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If DNN parameters are adapted for a particular user to improve recognition accuracy, then recognition performance is improved, but the risk of overfitting increases due to limited user-specific training data
Solution Approach 1:
The patent applies Kullback-Leibler Divergence regularization to constrain parameter changes during adaptation. The regularization term penalizes large deviations of adapted parameters from original DNN parameters, ensuring that adaptation improves user-specific recognition accuracy while preventing overfitting to limited training data through controlled parameter evolution
Solution Approach 2:
The patent introduces KL divergence as an intermediary constraint between the original DNN parameters and the adapted parameters. This intermediary mechanism mediates the adaptation process by allowing parameter updates while maintaining a controlled distance from the original parameters, thus balancing personalization with generalization
2Measurement precision
If extensive user-specific training data is collected to improve adaptation, then recognition accuracy is improved, but the time and user effort required increases significantly
Solution Approach 1:
The patent performs preliminary adaptation using a small amount of user-specific data with KL divergence regularization pre-constraining the parameter search space. This preliminary action with constrained parameters enables effective adaptation with minimal training data, avoiding the need to collect extensive training data while still achieving user-specific recognition improvement
Solution Approach 2:
By regularizing parameter changes during adaptation, the system achieves effective personalization with minimal training data. The constrained parameter updates allow the model to adapt to user characteristics without requiring large amounts of training data, thus reducing the time and effort needed for data collection
3Measurement precision
If the DNN model is made more complex with more parameters to improve recognition, then recognition capability is improved, but the difficulty of adaptation increases
Solution Approach 1:
The patent applies KL divergence regularization to control parameter changes in complex DNN models during adaptation. This regularization approach makes adaptation of complex models feasible by constraining the parameter search space, preventing catastrophic forgetting and overfitting while still allowing the model to capture user-specific characteristics
Solution Approach 2:
The KL divergence constraint acts as an intermediary that mediates between the complex model parameters and the adaptation process. It provides a structured approach to adapting complex models by maintaining controlled deviations from original parameters, thus making the adaptation of high-capacity models manageable and reliable
Data Source
AI summary
Various technologies described herein pertain to conservatively adapting a deep neural network (DNN) in a recognition system for a particular user or context. A DNN is employed to output a probability distribution over models of context-dependent units responsive to receipt of captured user input. The DNN is adapted for a particular user based upon the captured user input, wherein the adaption is undertaken conservatively such that a deviation between outputs of the adapted DNN and the unadapted DNN is constrained.