Confidence-Guided Masking for Robust Speech Representation Learning
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Solution Overview
Problem
Deep learning-based ASR models tend to overfit training data, leading to difficulties in generalizing unseen data, especially when the training data is not extensive, and domain mismatch results in reduced performance.
Innovation Solution
Implement guided data selection for masked speech modeling by using a masking module that processes encoded representations to generate probability distributions and confidence scores, selectively masking relevant frames for pre-training, and incorporating contrastive and cross-entropy losses to refine the audio encoder.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If deep learning-based ASR models are trained on limited training data, then training time and computational resources are reduced, but the models overfit the training data and fail to generalize to unseen data
Solution Approach 1:
The patent applies pre-training on large-scale unlabeled data before fine-tuning on smaller labeled datasets. This preliminary action enables the model to learn robust speech representations that generalize better to unseen data, directly addressing the overfitting problem when training data is limited
Solution Approach 2:
The patent introduces self-supervised learning as an intermediary training stage between traditional supervised learning and deployment. This intermediary approach uses contrastive loss and cross-entropy loss on unlabeled data to bridge the gap, enabling better generalization without requiring extensive labeled training data
2Reliability
If ASR models are trained on larger training datasets to improve accuracy, then generalization improves, but training complexity and computational resources increase
Solution Approach 1:
The patent employs self-supervised learning where the model generates its own training signals from unlabeled data through contrastive prediction and cross-entropy objectives. This self-service approach eliminates the need for extensive manual labeling, improving model accuracy without proportionally increasing training complexity
Solution Approach 2:
The patent segments the training process into distinct phases: pre-training on unlabeled data using self-supervised objectives, followed by fine-tuning on labeled data. This segmentation allows the model to achieve high accuracy while managing computational resources efficiently by using different training strategies for different data types
3Productivity
If domain mismatch occurs between training data and deployment scenarios, then training data utilization is efficient, but performance on unseen domains deteriorates
Solution Approach 1:
The patent performs pre-training on large-scale diverse unlabeled data from multiple domains before domain-specific fine-tuning. This preliminary exposure to varied data distributions enhances the model's adaptability to unseen domains while maintaining training efficiency through the use of self-supervised objectives
Solution Approach 2:
The patent changes training parameters and objectives based on data availability and domain requirements. By dynamically adjusting between contrastive loss, cross-entropy loss, and supervised objectives, the model achieves both training efficiency and domain adaptability across different scenarios
Data Source
AI summary
A method of guided data selection for masked speech modeling includes obtaining a sequence of encoded representations corresponding to an utterance. For each respective encoded representation, the method includes processing the respective encoded representation to generate a corresponding probability distribution over possible speech recognition hypotheses and assigning, to the respective encode representation, a confidence score as a highest probability from the corresponding probability distribution over possible speech recognition hypotheses. The method also includes selecting a set of unmasked encoded representations to mask based on the confidence scores assigned to the sequence of encoded representations. The method also includes generating a set of masked encoded representations by masking the selected set of unmasked encoded representations. Here, each masked encoded representation in the set of masked encoded representations corresponds to a respective one of the unmasked encoded representations in the selected set of unmasked encoded representations.