Ambient Audio Processing: From Sound to Structured Note
By Dr Youssef Ghaly and Dr Mostafa Helmy · Published August 2025
Abstract
This white paper traces how a live clinical conversation becomes a structured clinical note, walking through ClinixSummary's six-stage processing pipeline: audio capture and preprocessing, speech recognition, speaker diarisation, medical named entity recognition, section classification and clinical reasoning, and document assembly. It explains the clinical intelligence behind each stage and reports typical performance figures, including end-to-end processing of under 30 seconds for a standard 15-minute consultation. The paper concludes that a purpose-built pipeline integrating speech technology, medical NLP, and clinical domain knowledge lets clinicians focus on patients while the system handles documentation.
What the paper covers
A six-stage pipeline from audio to note
Processing follows six stages, each building on the last: audio capture and preprocessing, speech recognition and transcription, speaker diarisation, medical NER, section classification and clinical reasoning, and document assembly. End-to-end latency from final audio segment to complete note is typically under 30 seconds for a standard 15-minute consultation.
Medical speech recognition built for the clinic
The medical ASR model uses a 250,000+ medical term vocabulary with specialty-specific pronunciation variants, a contextual language model that biases toward clinically probable terms (e.g. "dyspnoea" over "Disney" in a respiratory consultation), real-time code-switching for multilingual consultations, and robust handling of abbreviations, dosages, lab values, and vital signs. Audio-to-transcript streaming latency is under 500ms.
Speaker diarisation with clinical role assignment
The diarisation system supports 2-4 speakers per session (clinician, patient, family member, interpreter), uses voice embeddings trained on clinical dialogue, assigns clinical roles from speech patterns and terminology usage, and handles overlapping speech. The paper states >95% diarisation accuracy on 2-speaker consultations.
Medical NER with coding and negation detection
A specialised NER model extracts diagnoses mapped to ICD-10, medications with dosage/route/frequency/duration, procedures mapped to CPT where applicable, anatomical references and laterality, lab values with units, temporal markers, and negations ("no chest pain", "denies fever"). Stated F1 score is >0.93 across core entity types.
Section classification that follows conversational flow
The section classifier goes beyond keyword matching: from a patient saying "I've been having this pain in my chest for about two weeks, it gets worse when I lie down," it files the content under History of Present Illness and extracts chest pain with a two-week duration and "worse when supine" as a modifier, even though the patient never used clinical terms. Stated accuracy is >91% on standard SOAP sections.
Document assembly and quality safeguards
The final stage selects templates by specialty, note type, and clinician preference, orders sections per documentation standards, normalises colloquial language into clinical terminology while preserving meaning, flags potentially missing sections or information, and suggests ICD-10/CPT codes from the extracted entities.
Figures and statements reflect the paper as published in August 2025.