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Related Experiment Video

Updated: Jun 5, 2026

Systematic Hearing Performance Evaluation Process for Adolescents with Cochlear Implantation at Early Ages
06:04

Systematic Hearing Performance Evaluation Process for Adolescents with Cochlear Implantation at Early Ages

Published on: March 24, 2023

Does Recording Hardware Matter for Clinical Speech Recognition? Evaluating ASR Performance Across Consumer Devices.

Brian D Tran, Di Hu, Seungjun Kim

    Medrxiv : the Preprint Server for Health Sciences
    |June 4, 2026
    PubMed
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    Ambient clinical intelligence (ACI) systems rely on automatic speech recognition (ASR) for clinical documentation. Our study found ASR engine choice impacts transcription more than recording devices, suggesting model selection is key for real-world ACI performance.

    Area of Science:

    • Medical Informatics
    • Speech Technology
    • Clinical Documentation

    Background:

    • Ambient clinical intelligence (ACI) systems leverage automatic speech recognition (ASR) to transcribe patient-provider conversations for clinical documentation.
    • Current ASR evaluations often occur under controlled settings with specialized equipment, potentially not reflecting real-world usage.

    Purpose of the Study:

    • To assess the impact of various recording devices on the transcription performance of contemporary ASR engines in a clinical dialogue context.
    • To compare the influence of recording hardware versus ASR engine choice on transcription accuracy.

    Main Methods:

    • Thirty-five primary care encounters were re-enacted and simultaneously recorded using five distinct devices: smartphone, laptop microphone, portable recorder, clip-on microphone, and desktop microphone.

    Related Experiment Videos

    Last Updated: Jun 5, 2026

    Systematic Hearing Performance Evaluation Process for Adolescents with Cochlear Implantation at Early Ages
    06:04

    Systematic Hearing Performance Evaluation Process for Adolescents with Cochlear Implantation at Early Ages

    Published on: March 24, 2023

  • Six different ASR engines were evaluated using word error rate (WER), clinical concept extraction (precision and recall), and sentence-level semantic similarity metrics.

    • Main Results:

    • Median word error rates (WER) across ASR engines ranged from 16.7% to 20.7%.
    • While recording device differences were statistically significant, the choice of ASR engine demonstrated a greater impact on transcription performance.
    • ASR engines showed considerable robustness when used with consumer-grade recording hardware.

    Conclusions:

    • Contemporary ASR engines exhibit resilience to variations in consumer-grade recording devices for clinical applications.
    • ASR model selection appears to be a more critical factor than recording device configuration for optimizing transcription performance in real-world ACI deployments.