Benchmarking Automatic Speech Recognition coupled LLM Modules for Medical Diagnostics
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arXiv
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866916621341163520 |
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| author | Kumar, Kabir |
| author_facet | Kumar, Kabir |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_13982 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Benchmarking Automatic Speech Recognition coupled LLM Modules for Medical Diagnostics Kumar, Kabir Audio and Speech Processing Machine Learning 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. |
| title | Benchmarking Automatic Speech Recognition coupled LLM Modules for Medical Diagnostics |
| topic | Audio and Speech Processing Machine Learning |
| url | https://arxiv.org/abs/2502.13982 |