Audio-Adapter Fusion for Efficient Multi-Task Speech Recognition
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Solution Overview
Problem
Existing automatic speech recognition (ASR) models are inflexible and computationally expensive to adapt to multiple tasks, leading to inaccuracies and inefficiencies due to their generalization across diverse user groups and the high computational cost of fine-tuning for specific tasks.
Innovation Solution
Implementing Audio-Adapter Fusion (A-AF) by combining parallel adapters trained on different tasks within multi-head attention layers of an audio encoder, allowing for efficient multi-task adaptation without requiring a task ID, thus enabling a single model to handle various speech recognition tasks effectively.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a single large ASR model is used for multiple tasks, then model generalization is improved, but adaptability to specific tasks deteriorates
Solution Approach 1:
The model is segmented into a shared base encoder and task-specific adapter modules. The adapter modules are inserted at specific attention layers to provide task-specific adaptations while sharing the computational backbone, thus improving both generalization and task-specific accuracy.
Solution Approach 2:
The base encoder serves as a universal component that handles general speech recognition across multiple tasks. Task-specific adapters are then applied to this universal base to enable multi-functionality for different speech recognition scenarios.
2Manufacturing precision
If multiple smaller ASR models are used for different tasks, then task-specific accuracy is improved, but computational cost deteriorates
Solution Approach 1:
Multiple task-specific models are merged into a single unified model by sharing the base encoder. Task-specific adapters are combined and inserted at the same attention layers, allowing multiple functions to be executed through one model rather than requiring separate models for each task.
Solution Approach 2:
A single ASR model is designed to perform multiple speech recognition tasks by incorporating task-specific adapters into a universal base encoder, eliminating the need for multiple separate models and reducing computational overhead.
3Adaptability or versatility
If fine-tuning is applied to adapt the model to specific tasks, then adaptability is improved, but training time and computational resources deteriorate
Solution Approach 1:
Task-specific adaptation capabilities are extracted from the entire model and isolated into small adapter modules that are inserted at specific attention layers. This allows fine-tuning to be applied only to these small adapter components rather than the entire large model, significantly reducing training time and computational resources.
Solution Approach 2:
Instead of fine-tuning all model parameters, only the parameters of the task-specific adapter modules are trained. This selective parameter training approach maintains adaptability while dramatically reducing the computational burden and training time required.
Data Source
AI summary
A method includes obtaining an audio encoder pre-trained on an initial training data set. The audio encoder includes a plurality of multi-head attention layers. The method also includes obtaining a first adapter corresponding to a first task and obtaining a second adapter corresponding to a second task. The operations also include adapting the audio encoder for the first task and the second task by inserting, in parallel, the first adapter and the second adapter at one or more of the plurality of multi-head attention layers of the audio encoder.