Speech Recognition Model Training with Contrastive Pre-Training
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Solution Overview
Problem
Existing speech recognition models rely heavily on large amounts of labeled data and are limited by the connectionist temporal classification (CTC) framework, leading to suboptimal performance and recognition accuracy.
Innovation Solution
A method involving self-supervised training using a contrastive learning loss function on unlabeled data for the encoder network, followed by joint loss function training on labeled data for both networks, allowing for fine-tuning without excessive reliance on labeled data and breaking free from the CTC framework.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional speech recognition models use CTC framework with labeled data, then model training can be performed, but recognition accuracy is limited and labeled data costs are high
Solution Approach 1:
The patent applies preliminary action by performing self-supervised pre-training on the encoder using unlabeled data before fine-tuning with labeled data. This preliminary pre-training phase allows the model to learn robust speech representations without requiring extensive labeled data, thereby reducing labeled data costs while improving final recognition accuracy.
Solution Approach 2:
The patent employs parameter changes by transitioning from fixed CTC framework parameters to flexible attention mechanism parameters. The model dynamically adjusts attention weights during decoding, allowing it to capture temporal dependencies more effectively than fixed CTC alignments, thus improving recognition accuracy without proportionally increasing labeled data requirements.
2Ease of manufacture
If CTC framework is used for training, then model training is simplified, but performance and recognition accuracy are suboptimal
Solution Approach 1:
The patent segments the training process into two distinct phases: (1) self-supervised pre-training phase using unlabeled data with contrastive learning, and (2) supervised fine-tuning phase using labeled data with joint loss function. This segmentation allows each phase to optimize for its specific goal, maintaining training manageability while achieving superior accuracy compared to unified CTC training.
Solution Approach 2:
The patent introduces an attention mechanism as an intermediary between the encoder and decoder, replacing the direct CTC alignment. This intermediary allows the model to dynamically weigh different temporal positions, capturing dependencies that CTC misses, thereby improving accuracy while the two-stage training structure maintains ease of implementation.
3Quantity of substance
If self-supervised training on unlabeled data is performed, then labeled data costs are reduced, but training process complexity increases
Solution Approach 1:
The patent applies self-service by implementing self-supervised learning where the model generates its own training signals from unlabeled speech data through contrastive learning. The encoder learns to distinguish between different speech representations without external labels, making the system self-sufficient for the pre-training phase and significantly reducing dependency on expensive labeled data.
Solution Approach 2:
The patent uses preliminary action by completing the heavy lifting of learning speech representations in advance using unlabeled data before the final supervised fine-tuning. This preliminary pre-training handles the complexity of unsupervised learning separately, allowing the subsequent labeled training to be simpler and more focused on task-specific optimization.
4Measurement precision
If joint loss function training is performed on both networks, then model performance improves, but training time increases
Solution Approach 1:
The patent applies preliminary action by performing encoder pre-training on unlabeled data before joint fine-tuning. This preliminary phase prepares the encoder with robust representations, so that the subsequent joint loss function training on both encoder and decoder requires fewer iterations and less time to converge, reducing overall training time while maintaining performance improvements.
Solution Approach 2:
The patent segments training into phases with different data requirements: unsupervised pre-training (fast, uses unlabeled data) followed by supervised fine-tuning (slower, uses labeled data). This segmentation allows the majority of training to occur in the faster unsupervised phase, reducing the time spent in the slower joint fine-tuning phase while still achieving performance gains.
Data Source
AI summary
A method for training a speech recognition model, includes: constructing an initial speech recognition model including a first network having a first initial parameter and a second network having a second initial parameter; fixing the second initial parameter, calculating a contrastive learning loss function, and performing self-supervised training on the first network according to the contrastive learning loss function to adjust the first initial parameter to a first intermediate parameter; fixing the first intermediate parameter, calculating a first joint loss function, and performing training on the second network according to the first joint loss function to adjust the second initial parameter to a second intermediate parameter; and calculating a second joint loss function, and performing training an the first network and the second network according to the second joint loss function to adjust the first intermediate parameter and the second intermediate parameter to obtain a target speech recognition model.