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A knowledge-driven framework for surgical safety check integration using speech recognition and speaker verification.

Wen Shi1, Rui Fan1, Jian Hu1

  • 1Air Force Medical University Tangdu Hospital, Xi'an, China.

Frontiers in Neuroscience
|January 28, 2026
PubMed
Summary

A novel knowledge-driven framework integrating automatic speech recognition and speaker verification improves surgical safety checklist accuracy in noisy operating rooms. This system enhances reliability by considering speech content, speaker identity, and checklist rules.

Keywords:
automatic speech recognitionknowledge-driven reasoningoperating room noise robustnessspeaker verificationsurgical safety checklist

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Area of Science:

  • Medical Informatics
  • Speech Recognition
  • Artificial Intelligence

Background:

  • Operating rooms present significant audio challenges including overlapping speech and high noise levels.
  • Existing automatic speech recognition (ASR)-only systems struggle with accuracy due to these factors and lack of speaker identity.
  • The WHO Surgical Safety Checklist aims to reduce errors but is vulnerable to misrecognition in real-world OR environments.

Purpose of the Study:

  • To develop and evaluate an integrated framework combining ASR and speaker verification (SV) with a knowledge-driven rule engine for surgical safety.
  • To enhance the accuracy and reliability of checklist item verification in complex operating room audio conditions.
  • To provide real-time system prompts indicating checklist status (pass, fault, alarm, uncertain).

Main Methods:

  • An integrated ASR+SV framework was designed, incorporating a knowledge-driven rule engine based on the WHO checklist.
  • Multichannel audio was processed using Conformer-based ASR and ECAPA-TDNN SV models.
  • A rule layer enforced consistency using an ontology and conflict-resolution rules, comparing ASR-only, ASR+SV, and the full knowledge-driven approach.

Main Results:

  • The full framework reduced Word Error Rate (WER) from 18.7% to 13.5% and achieved an Equal Error Rate (EER) of 3.1% under noisy, multi-speaker conditions.
  • Checklist verification accuracy reached 93.8% with an alarm rate of approximately 2.7%.
  • The system demonstrated robust performance up to 1.5m speaking distance and 60dB background noise, correcting semantic and role-based errors.

Conclusions:

  • The knowledge-driven ASR+SV framework offers an interpretable and auditable solution for intelligent operating rooms, improving semantic correctness and speaker verification.
  • It provides a foundation for enhanced "time-out" and "operation review" functions.
  • Future work includes clinical pilot studies and integration with multimodal OR data, addressing privacy and workflow acceptance.