Multimodal SLU Using Audio Cues and Text for Semantic Accuracy
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Solution Overview
Problem
Existing Spoken Language Understanding (SLU) systems face challenges in efficiently leveraging both audio and textual data for accurate semantic representation, particularly in specific domains, due to the high cost and inefficiency of training new ASR modules and the limitations of using only textual data for fine-tuning pre-trained language models.
Innovation Solution
The proposed solution involves fine-tuning a pre-trained large language model (LLM) using both audio and textual data, leveraging a pre-trained audio encoder for self-supervised training and incorporating non-verbal cues from audio data to enhance semantic understanding, thus improving performance in specified domains with reduced computational and time costs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a new ASR module is trained using a large amount of annotated speech data for a specific domain, then the ASR performance for that domain is improved, but the cost (computational resources, time, effort) increases significantly
Solution Approach 1:
The system performs preliminary action by using a pre-trained LLM that has already been trained on large amounts of general language data. This pre-trained model serves as a foundation that can be quickly adapted to specific domains through fine-tuning with minimal domain-specific data, avoiding the need to train from scratch and thus reducing training time and computational resources while maintaining good ASR performance.
Solution Approach 2:
The system creates a copy of the pre-trained LLM architecture and fine-tunes it for specific domains. Instead of training a completely new ASR module, the approach copies the proven pre-trained model structure and adapts it through efficient fine-tuning processes, significantly reducing the computational burden and time required while achieving domain-specific performance.
2Productivity
If only textual data from transcripts is used for fine-tuning the pre-trained LLM, then the fine-tuning process is simpler and faster, but the semantic understanding accuracy is reduced due to loss of audio information
Solution Approach 1:
The system segments the input data into two distinct streams: audio data and textual transcript data. Each stream is processed separately through dedicated encoders (audio encoder and text encoder respectively), allowing the system to maintain the simplicity and speed of text processing while simultaneously preserving and utilizing audio information for enhanced semantic understanding. The segmented processing enables efficient parallel computation.
Solution Approach 2:
The system adds another dimension to the processing by incorporating audio data as a separate input modality alongside textual data. Instead of relying solely on the one-dimensional text input, the system processes audio signals through a dedicated audio encoder to extract paralinguistic features, prosody, and other audio-based semantic cues, creating a multi-dimensional representation that enriches semantic understanding while maintaining processing efficiency through modular architecture.
Data Source
AI summary
In various implementations, a method implemented by one or more processors of a computing device can comprise receiving audio data that captures a spoken utterance of a user; processing the audio data using an automatic speech recognition (ASR) model to generate textual data corresponding to the spoken utterance; generating a semantic representation corresponding to the spoken utterance of the user based on applying both the audio data and the textual data as input across a large language model (LLM); and causing the semantic representation corresponding to the spoken utterance of the user to be utilized in fulfilling the spoken utterance.