Domain-Specific Parameter Pre-fixes for ASR Adaptation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing automatic speech recognition (ASR) systems face challenges in adapting to domain-specific tasks due to the need for large memory and computational resources, especially in low-resource settings, where maintaining multiple domain-adapted language models is not scalable and generic models fall short in performance.
Innovation Solution
Implementing domain-specific parameter pre-fixes for transformer-based language models, which prime a pre-trained model with a small set of domain-specific parameters to improve ASR performance without the need for extensive resource allocation, allowing for efficient domain adaptation across various domains.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple domain-adapted language models are maintained to improve ASR performance in different domains, then recognition accuracy is improved, but memory and computational resources increase significantly
Solution Approach 1:
The language model is segmented into domain-general parameters and domain-specific parameters. Only domain-specific parameters are stored and loaded for each domain, while the core model remains shared. This segmentation allows multiple domain adaptations without proportionally increasing total model size, resolving the contradiction between accuracy and resource usage.
Solution Approach 2:
A single base language model serves multiple domains through parameter adaptation. The domain-general model can be adapted to different domains by loading different domain-specific parameter sets, making one model perform multiple domain-specific functions. This universality reduces the need for maintaining separate full models for each domain.
2Measurement precision
If domain-specific parameter pre-fixes are used to improve ASR performance, then recognition accuracy approaches that of custom-trained models, but training time and computational overhead are reduced
Solution Approach 1:
Domain-specific parameters are pre-computed and stored before actual use. The computationally intensive work of domain adaptation is performed in advance during an offline training phase, creating pre-fixed parameters that can be quickly loaded and applied. This preliminary action separates the heavy computational burden from the actual deployment time.
Solution Approach 2:
Instead of training complete domain-specific models from scratch, the invention creates parameter copies or adaptations from a base model. Domain-specific parameters are derived as adaptations of the general model parameters, allowing rapid domain specialization without repeating the full training process for each domain.
Data Source
AI summary
Domain-specific parameters may be used for tuning speech processing. A pre-trained transformer-based language model may train domain-specific parameters using domain-specific unlabeled text data. This domain-specific parameters can then be appended to candidate texts produced by a speech model on received speech data and input to the transformer-based language model to score the candidate texts. The scores of the candidate texts determined using the pre-trained transformer-based language model can then be used to select a candidate text for further speech processing.