Benchmarking Automatic Speech Recognition coupled LLM Modules for Medical Diagnostics
This addresses the problem of efficient and accessible healthcare support for patients and professionals, though it appears incremental as it builds on existing ASR and LLM technologies.
The paper tackles the problem of automating medical diagnostics by developing a two-stage system that combines Automatic Speech Recognition (ASR) finetuned on medical call recordings with a Large Language Model (LLM) for diagnosis, using a novel audio preprocessing strategy to handle diverse recording conditions.
Natural Language Processing (NLP) and Voice Recognition agents are rapidly evolving healthcare by enabling efficient, accessible, and professional patient support while automating grunt work. This report serves as my self project wherein models finetuned on medical call recordings are analysed through a two-stage system: Automatic Speech Recognition (ASR) for speech transcription and a Large Language Model (LLM) for context-aware, professional responses. ASR, finetuned on phone call recordings provides generalised transcription of diverse patient speech over call, while the LLM matches transcribed text to medical diagnosis. A novel audio preprocessing strategy, is deployed to provide invariance to incoming recording/call data, laden with sufficient augmentation with noise/clipping to make the pipeline robust to the type of microphone and ambient conditions the patient might have while calling/recording.