用语音识别+大模型自动诊断病情，提升医疗咨询效率。
Benchmarking Automatic Speech Recognition coupled LLM Modules for Medical Diagnostics
- 两阶段流程：先语音转文字，再匹配医学诊断。
- 通过噪声和剪辑增强，使系统适应不同录音设备。
- 适合医疗自动化、智能问诊系统开发者参考。
自然语言处理与语音识别技术正快速推动医疗发展，实现高效、可及且专业的患者支持，并自动化重复性工作。本报告为个人项目，分析了在医疗通话录音上微调的模型，采用两阶段系统：自动语音识别（ASR）将通话语音转写为文本，大型语言模型（LLM）基于转写内容生成上下文感知的医学诊断建议。ASR在电话通话录音上微调，实现对多样化患者语音的通用转写；LLM则将转写文本与医学诊断进行匹配。引入一种新型音频预处理策略，通过添加噪声和截断等增强手段，使系统对不同麦克风和环境噪音具备鲁棒性。
原文摘要 · Abstract (English)
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.
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