Deep Neural Network Parameter Tuning for Domain Adaptation
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Solution Overview
Problem
Existing deep neural networks (DNNs) for recognition processing, such as ASR, face accuracy deterioration when adapting to out-of-domain data due to the need for additional computation and supervised information, and often require modifying network topology or labor-intensive data preparation.
Innovation Solution
A method that adjusts the parameters of lower layers in a DNN to transform target-domain data into the distribution space of the training domain, leveraging the discriminative capability of the remaining network without additional computation or supervised information, allowing for improved accuracy without modifying the network topology.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If linear transformations or adaptation neural networks are applied to improve accuracy for target-domain data, then recognition accuracy is improved, but additional computation during run-time is required
Solution Approach 1:
The invention performs adaptation computations during the offline training phase by minimizing the distance between feature distributions from training-domain and target-domain data. This preliminary action transfers the computational burden to the training stage, allowing the adapted model to run efficiently during recognition processing without additional real-time computation requirements.
2Measurement precision
If supervised adaptation is conducted to improve accuracy, then recognition accuracy is improved, but additional costs and labor-intensive data preparation are required
Solution Approach 1:
The invention employs unsupervised distribution matching that automatically adapts the model to target-domain data without requiring manually annotated training data. The system self-adjusts by minimizing the distance between feature distributions, eliminating the need for labor-intensive supervised data preparation while maintaining adaptation effectiveness.
3Adaptability or versatility
If additional units or layers are added to the DNN for adaptation, then adaptation capability is improved, but device complexity increases
Solution Approach 1:
The invention achieves domain adaptation by adjusting the parameters (weights and biases) of existing network layers rather than adding new units or layers. This parameter-level adaptation modifies the feature extraction behavior to match target-domain distributions while preserving the original network architecture, thus improving adaptability without increasing structural complexity.
Data Source
AI summary
A computer implemented method for adapting a model for recognition processing to a target-domain is disclosed. The method includes preparing a first distribution in relation to a part of the model, in which the first distribution is derived from data of a training-domain for the model. The method also includes obtaining a second distribution in relation to the part of the model by using data of the target-domain. The method further includes tuning one or more parameters of the part of the model so that difference between the first and the second distributions becomes small.