Cross-Attention Textual Knowledge Transfer for Speech Understanding
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing spoken language understanding systems face challenges in integrating dialogue history effectively, leading to large model sizes and performance degradation due to the use of both automatic speech recognition and natural language understanding components, and there is a lack of efficient knowledge transfer from text-based models like BERT to speech-based models for improved semantic understanding.
Innovation Solution
A system that integrates fine-grained textual knowledge transfer by aligning Large Language Model (LLM) embeddings with speech-based embeddings using a cross-attention mechanism, combining alignment and ASR losses, and utilizing a self-attention layer for Spoken Language Understanding (SLU) tasks, enabling compact models to leverage BERT knowledge during pretraining.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If both automatic speech recognition and natural language understanding components are used, then speech recognition accuracy is improved, but model size increases and performance degrades
Solution Approach 1:
The patent combines ASR and SLU components into a single integrated model architecture, merging previously separate processing stages into one unified system that shares parameters and representations, thereby reducing overall model size while maintaining accuracy
Solution Approach 2:
The integrated model performs multiple functions (speech recognition and language understanding) within a single architecture, allowing the same components to serve dual purposes and reducing the need for separate specialized modules
2Measurement precision
If text-based models like BERT are integrated with speech-based models, then semantic understanding is improved, but knowledge transfer efficiency is insufficient
Solution Approach 1:
The patent applies preliminary action by pre-training the speech encoder using text-based BERT embeddings as supervision signals before fine-tuning on speech data. This preliminary pre-training step efficiently transfers linguistic knowledge from BERT to the speech model, improving semantic understanding without requiring direct integration of the large BERT model during inference
Solution Approach 2:
The patent uses speech embeddings as an intermediary representation that bridges the gap between text-based BERT knowledge and speech-based processing. The BERT embeddings serve as a mediator to transfer knowledge indirectly through the speech encoder, avoiding direct coupling while enabling efficient knowledge transfer
3Measurement precision
If dialogue history is integrated into spoken language understanding, then understanding accuracy is improved, but system complexity increases
Solution Approach 1:
The patent merges dialogue history integration directly into the SLU model architecture by incorporating historical utterance representations into the same neural network layers that process current utterances, eliminating the need for separate dialogue management modules and reducing system complexity
Data Source
AI summary
Systems, computer-implemented methods, and computer program products to facilitate fine-grained textual knowledge transfer to improve speech recognition and understanding are provided. According to an embodiment, a system can comprise a processor that executes components stored in memory. The computer executable components comprise deriving component that can derive one or more speech-based embeddings from an utterance via a speech encoder. The computer executable components can comprise a cross-attention component that can align, at a token level, one or more large language model (LLM) based sentence embeddings with the one or more speech-based embeddings. The computer executable components can comprise a loss component that can combine an alignment loss and an automatic speech recognition (ASR) loss.