Hierarchical Recurrent Adapters for Multi-Task Speech Models
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Solution Overview
Problem
Existing automatic speech recognition (ASR) models are inflexible and computationally expensive to adapt for multiple tasks, especially when dealing with atypical speech or emerging words/phrases.
Innovation Solution
The implementation of a hierarchical recurrent adapter (HRA) that includes a shared controller and multiple task-level adapter heads, allowing for efficient multi-task adaptation of large ASR models while keeping the adapter parameter overhead minimal.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If multiple smaller ASR models are used to improve flexibility for different tasks, then adaptability is improved, but computational cost and training redundancy increase
Solution Approach 1:
The patent implements a universal adapter structure that can be shared across multiple tasks and domains. The adapter includes a controller and multiple adapter heads that can handle different speech recognition tasks (e.g., atypical speech, emerging words/phrases) without requiring separate models, thus reducing computational cost while maintaining flexibility.
Solution Approach 2:
The adapter is nested within the pre-trained ASR model architecture, with adapter components inserted at specific layers. This nested structure allows the adapter to leverage the existing model's computational infrastructure while adding task-specific adaptation capabilities, avoiding the need for completely separate models.
2Measurement precision
If the ASR model is fine-tuned to a specific task to improve performance, then recognition accuracy is improved, but scalability to multiple tasks deteriorates
Solution Approach 1:
The patent segments the adaptation function into separate adapter components (controller and adapter heads) that can be independently configured for different tasks. This segmentation allows the same base model to be adapted to multiple tasks by switching or combining different adapter configurations, maintaining both accuracy and scalability.
Solution Approach 2:
The adapter implements dynamic task switching capabilities where the controller can selectively activate different adapter heads based on the current task requirements. This dynamic configuration allows the system to maintain high accuracy for specific tasks while preserving the ability to scale to multiple different tasks.
3Reliability
If a large pre-trained model is used to improve robustness, then model reliability is improved, but training data requirements and training time increase
Solution Approach 1:
The patent applies preliminary pre-training to a large ASR model on extensive general speech data to establish robust foundational capabilities. Then, instead of requiring additional large-scale task-specific training data, the adapter is used for efficient task adaptation, thereby reducing the overall training data requirements while maintaining model robustness.
Solution Approach 2:
The adapter acts as an intermediary between the pre-trained model and task-specific requirements. It bridges the gap by providing task-specific adaptation without requiring the base model to be retrained on task-specific data, thus reducing training data requirements while maintaining robustness.
4Measurement precision
If the ASR model is fine-tuned to a specific task to improve performance, then recognition accuracy is improved, but computational expense and scalability deteriorate
Solution Approach 1:
The patent extracts the task-specific adaptation functionality into a separate, lightweight adapter module that is distinct from the main pre-trained model. This extraction allows the heavy computational burden of the base model to remain fixed while only the small adapter components require task-specific training, significantly reducing computational expense for multi-task adaptation.
Solution Approach 2:
The adapter implements a cost-effective adaptation mechanism where lightweight adapter heads can be quickly trained or configured for different tasks without the high computational expense of fine-tuning the entire large model. This approach treats adaptation as a low-cost operation that can be performed efficiently for multiple tasks.
Data Source
AI summary
A method for implementing hierarchical recurrent adapters for efficient multi-task adaptation of large speech models including obtaining an automatic speech recognition (ASR) model pre-trained on an initial training data set, the ASR model including a plurality of layers. The method includes augmenting the ASR model with a recurrent adapter including a controller and a plurality of adapter heads, wherein the controller and the plurality of adapter heads are shared with each layer of the plurality of layers of the ASR model. The method also includes receiving an adaptation training data set including a plurality of spoken utterances, each respective spoken utterance paired with a respective transcription of the respective spoken utterance. The method includes adapting the ASR model augmented with the recurrent adapter to the adaptation training data set while parameters of the ASR model are frozen.