ASR Joint Training With Bilevel Optimization for Negative Transfer
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Solution Overview
Problem
Conventional automatic speech recognition (ASR) models rely heavily on labeled data, which is expensive to obtain, leading to inefficiencies in training due to disconnected pre-training and fine-tuning loops, limited control over knowledge transfer, and potential negative transfer issues.
Innovation Solution
A joint unsupervised and supervised training method using bilevel optimization, where a backbone model parameter and classification head parameter are initialized randomly and updated through gradient descent with respect to unsupervised and supervised losses, facilitating a single-loop training process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If pre-training and fine-tuning are performed in separate disconnected loops, then the model can be trained on large unlabeled data and then adapted to downstream tasks, but the training complexity and processing time increase significantly
Solution Approach 1:
The patent merges pre-training and fine-tuning into a single unified training loop that simultaneously optimizes both unsupervised and supervised objectives. This is achieved by formulating the problem as a bilevel optimization where the upper level performs supervised fine-tuning while the lower level performs unsupervised pre-training, allowing both processes to occur concurrently rather than sequentially, thus reducing training complexity while maintaining model adaptability
2Ease of manufacture
If pre-training is performed independently without feedback from downstream tasks, then the pre-training process is simpler, but the fine-tuning step has limited control over the upstream pre-training and knowledge transfer is suboptimal
Solution Approach 1:
The patent implements a feedback mechanism where the supervised fine-tuning loss from the upper level provides gradient information that guides the unsupervised pre-training in the lower level. This feedback loop allows the downstream task requirements to influence the pre-training process, ensuring that the learned representations are more suitable for the specific downstream applications while maintaining the benefits of unsupervised learning on large datasets
3Reliability
If conventional supervised training is used with large labeled datasets, then the model achieves good performance, but labeled data is expensive to obtain and processing time increases
Solution Approach 1:
The patent performs unsupervised pre-training as a preliminary action before supervised fine-tuning, but within a unified optimization framework. The lower level of the bilevel optimization learns general speech representations from unlabeled data in advance, which then serves as a foundation for the supervised task. This preliminary unsupervised learning reduces the amount of labeled data needed and accelerates the overall training process while maintaining high ASR performance
Data Source
AI summary
A backbone model parameter and a classification head parameter are randomly initialized. A gradient descent is applied to a lower-level unsupervised loss with respect to the initialized backbone model parameter and the initialized backbone model parameter is updated. A gradient descent is applied to a higher-level supervised loss and the initialized classification head parameter is updated. Deployment of the updated backbone model parameter and the updated classification head parameter are facilitated.