Speech Adapter Fusing Representation with Pre-trained Model for LLM Tasks
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Solution Overview
Problem
Existing multi-modal large language models (LLMs) are limited in processing speech-related tasks, such as understanding speaker emotions, speech content, or the number of speakers, as they primarily focus on audio-related tasks without integrating specific speech processing capabilities.
Innovation Solution
A method and device for processing speech that involves obtaining a speech input, generating a speech representation, fusing this representation with a pre-trained adapter using multi-head attention to create an adapter with speech information, and then using this adapter along with an instruction to generate a response through a language model.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If existing multi-modal LLMs are used for audio-related tasks, then audio processing capability is maintained, but speech-related task processing capability is insufficient
Solution Approach 1:
The patent introduces a speech adapter as an intermediary component that bridges the gap between existing multi-modal LLMs and speech-related tasks. This adapter layer is inserted between the speech encoder and the LLM, enabling the model to process speech-related tasks (emotion recognition, speaker recognition, etc.) without fundamentally altering the original architecture, thus maintaining audio processing capability while adding speech-specific functionality
Solution Approach 2:
The patent segments the speech processing pipeline into distinct components: a speech encoder for extracting speech features, a speech adapter for task-specific adaptation, and the existing LLM for response generation. This segmentation allows each component to be optimized independently, with the speech adapter specifically tailored for speech-related tasks while the LLM handles general language processing
2Loss of information
If a speech encoder is added to process speech inputs, then speech information extraction is improved, but model complexity increases
Solution Approach 1:
The speech adapter serves as an intermediary that simplifies the integration of the speech encoder with the existing LLM. Instead of requiring complex retraining or architectural changes to the entire model, the adapter layer provides a simple interface that translates speech encoder outputs into a format compatible with the LLM, thereby reducing the perceived complexity of integrating speech processing capabilities
Solution Approach 2:
The speech adapter is designed to be a universal component that can handle multiple speech-related tasks (emotion recognition, speaker recognition, speech translation, etc.) through a single unified architecture. This multi-functionality reduces the need for separate specialized modules for each task, thereby controlling model complexity while maintaining comprehensive speech information extraction
3Loss of information
If speech representation is fused with pre-trained adapter using multi-head attention, then speech information integration is improved, but computational cost increases
Solution Approach 1:
The patent applies multi-head attention selectively only to the speech adapter component rather than the entire model architecture. This partial application of the computationally intensive attention mechanism achieves effective speech information integration where it is most needed, while avoiding unnecessary computational overhead in other parts of the system that do not require such intensive processing
Data Source
AI summary
A method of processing speech includes: obtaining a speech input; obtaining an instruction related to the speech input; obtaining a speech representation corresponding to the speech input; obtaining an adapter that includes speech information by fusing a pre-trained adapter with the speech representation; and obtaining a response corresponding to the instruction by inputting both the adapter that includes the speech information and the instruction to a language model, the language model generating the response based on the adapter that includes the speech model and the speech information.