Multimodal LLM Spoken Understanding with Audio-Text Fine-Tuning
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Solution Overview
Problem
Existing Spoken Language Understanding (SLU) systems face challenges in efficiently adapting to specific domains due to the high cost and inefficiency of training large, pre-trained transformer-based language models, as they often rely on one-size-fits-all approaches, neglecting the benefits of audio data in fine-tuning, which limits the utilization of non-verbal cues and domain-specific performance.
Innovation Solution
The proposed solution involves fine-tuning a pre-trained large language model (LLM) using both textual and audio data, leveraging audio encoders pre-trained on domain-specific tasks, enabling the model to capture semantic information from audio cues like prosody and intonation, thus improving domain-specific performance while reducing the computational and time costs associated with bespoke ASR module development.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If a pre-trained LLM is fine-tuned using only textual data (transcripts), then the model can be trained with existing NLP resources, but audio data containing non-verbal cues and paralinguistics is lost
Solution Approach 1:
The patent merges audio data and textual data into a unified fine-tuning process for the LLM. The audio encoder processes audio inputs and the LLM processes both audio embeddings and text transcripts together, combining multiple data sources to achieve comprehensive semantic understanding that captures both verbal content and non-verbal cues.
Solution Approach 2:
The audio encoder acts as an intermediary component that converts audio data into embeddings that can be processed by the LLM. This mediator enables the LLM to utilize audio information without requiring direct audio processing capability, allowing seamless integration of audio and text data streams.
2Reliability
If a bespoke ASR module is trained for a specific domain, then domain-specific performance is improved, but computational resources and time required for training increase
Solution Approach 1:
The patent employs a universal pre-trained LLM that can be fine-tuned for multiple domains using the same base model architecture. Instead of training separate ASR modules for different domains, the system uses a single multi-functional LLM that adapts to various domains through fine-tuning on domain-specific audio-text pairs, reducing redundant computational resources.
Solution Approach 2:
The LLM is pre-trained on general-purpose data before being fine-tuned for specific domains. This preliminary training establishes a robust foundation that can be efficiently adapted to domain-specific tasks, reducing the computational resources needed for domain-specific training compared to training from scratch.
3Measurement precision
If audio data is incorporated into fine-tuning the LLM, then semantic understanding of spoken utterances is improved, but training data processing complexity increases
Solution Approach 1:
The patent segments the processing pipeline into distinct components: an audio encoder that processes audio data independently, and an LLM that processes both audio embeddings and text. This segmentation allows each component to be optimized separately and simplifies the overall training process by dividing the complex audio-text processing into manageable stages.
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.