000168535 001__ 168535
000168535 005__ 20260213183047.0
000168535 0247_ $$2doi$$a10.1016/j.jvoice.2025.11.037
000168535 0248_ $$2sideral$$a147955
000168535 037__ $$aART-2025-147955
000168535 041__ $$aeng
000168535 100__ $$aEstevez, Mariel$$uUniversidad de Zaragoza
000168535 245__ $$aBeyond Global Metrics: A Fairness Analysis for Interpretable Voice Disorder Detection Systems
000168535 260__ $$c2025
000168535 5060_ $$aAccess copy available to the general public$$fUnrestricted
000168535 5203_ $$aWe investigate and quantify demographic-dependent biases in automatic voice disorders detection (AVDD) systems by analyzing performance disparities across speaker groups, and evaluate group-specific calibration strategies for improving reliability. We conducted a comprehensive analysis of an AVDD system using existing voice disorder datasets with available demographic metadata. The study involved analyzing system performance across various demographic groups, particularly focusing on gender and age-based cohorts. Performance evaluation was based on multiple metrics, including normalized costs and cross-entropy. We employed calibration techniques trained separately on predefined demographic groups to address group-dependent miscalibration. Analysis revealed significant performance disparities across demographic groups despite strong global metrics. The system showed systematic biases, misclassifying healthy speakers over 55 as having a voice disorder and speakers with disorders aged 14–30 as healthy. Group-specific calibration improved posterior probability quality, reducing overconfidence. For young disordered speakers, low severity scores were identified as contributing to poor system performance. For older speakers, age-related voice characteristics and potential limitations in the pretrained Hubert model used as a feature extractor likely affected results. The study demonstrates that global performance metrics are insufficient for evaluating AVDD system performance. Group-specific analysis may unmask problems in system performance fairly which are hidden within global metrics. Further, group-dependent calibration strategies help mitigate biases, resulting in a more reliable indication of system confidence. These findings emphasize the need for demographic-specific evaluation and calibration in voice disorder detection systems while providing a methodological framework applicable to broader biomedical classification tasks where demographic metadata is available.
000168535 536__ $$9info:eu-repo/grantAgreement/ES/AEI/PID2021-126061OB-C44$$9info:eu-repo/grantAgreement/ES/DGA/T36-23R$$9info:eu-repo/grantAgreement/EC/H2020/101007666/EU/Exchanges for SPEech ReseArch aNd TechnOlogies/ESPERANTO$$9This project has received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement No H2020 101007666-ESPERANTO$$9info:eu-repo/grantAgreement/EC/H2020/101206575/EU/Mental Illness Detection and Clinical Assessment with Reliable Interpretability/MIND-CLARITY$$9This project has received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement No H2020 101206575-MIND-CLARITY
000168535 540__ $$9info:eu-repo/semantics/embargoedAccess$$aAll rights reserved$$uhttp://www.europeana.eu/rights/rr-f/
000168535 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/acceptedVersion
000168535 700__ $$aBonomi, Cyntia
000168535 700__ $$0(orcid)0000-0003-3813-4998$$aRibas, Dayana$$uUniversidad de Zaragoza
000168535 700__ $$0(orcid)0000-0002-3886-7748$$aOrtega, Alfonso$$uUniversidad de Zaragoza
000168535 700__ $$aFerrer, Luciana
000168535 7102_ $$15008$$2800$$aUniversidad de Zaragoza$$bDpto. Ingeniería Electrón.Com.$$cÁrea Teoría Señal y Comunicac.
000168535 773__ $$g(2025), [13 pp.]$$pJ. voice$$tJOURNAL OF VOICE$$x0892-1997
000168535 8564_ $$s5129498$$uhttps://zaguan.unizar.es/record/168535/files/texto_completo.pdf$$yPostprint$$zinfo:eu-repo/date/embargoEnd/2026-12-19
000168535 8564_ $$s1594639$$uhttps://zaguan.unizar.es/record/168535/files/texto_completo.jpg?subformat=icon$$xicon$$yPostprint$$zinfo:eu-repo/date/embargoEnd/2026-12-19
000168535 909CO $$ooai:zaguan.unizar.es:168535$$particulos$$pdriver
000168535 951__ $$a2026-02-13-18:30:21
000168535 980__ $$aARTICLE