000148274 001__ 148274 000148274 005__ 20250115160155.0 000148274 0247_ $$2doi$$a10.1016/j.dsp.2022.103536 000148274 0248_ $$2sideral$$a128724 000148274 037__ $$aART-2022-128724 000148274 041__ $$aeng 000148274 100__ $$aPrieto, S. 000148274 245__ $$aShouted and whispered speech compensation for speaker verification systems 000148274 260__ $$c2022 000148274 5060_ $$aAccess copy available to the general public$$fUnrestricted 000148274 5203_ $$aNowadays, speaker verification systems begin to perform very well under normal speech conditions due to the plethora of neutrally-phonated speech data available, which are used to train such systems. Nevertheless, the use of vocal effort modes other than normal severely degrades performance because of vocal effort mismatch. In this paper, in which we consider whispered, normal and shouted speech production modes, we first study how vocal effort mismatch negatively affects speaker verification performance. Then, in order to mitigate this issue, we describe a series of techniques for score calibration and speaker embedding compensation relying on logistic regression-based vocal effort mode detection. To test the validity of all of these methodologies, speaker verification experiments using a modern x-vector-based speaker verification system are carried out. Experimental results show that we can achieve, when combining score calibration and embedding compensation relying upon vocal effort mode detection, up to 19% and 52% equal error rate (EER) relative improvements under the shouted-normal and whispered-normal scenarios, respectively, in comparison with a system applying neither calibration nor compensation. Compared to our previous work 1], we obtain a 7.3% relative improvement in terms of EER when adding score calibration in shouted-normal All vs. All condition. © 2022 Elsevier Inc. 000148274 536__ $$9info:eu-repo/grantAgreement/ES/AEI/PDC2021-120846-C41$$9info:eu-repo/grantAgreement/ES/DGA/T36-20R$$9info:eu-repo/grantAgreement/EC/H2020/101007666/EU/Exchanges for SPEech ReseArch aNd TechnOlogies/ESPERANTO$$9This project has received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement No H2020 101007666-ESPERANTO$$9info:eu-repo/grantAgreement/ES/MICINN-AEI/10.13039/501100011033 000148274 540__ $$9info:eu-repo/semantics/openAccess$$aby-nc-nd$$uhttp://creativecommons.org/licenses/by-nc-nd/3.0/es/ 000148274 590__ $$a2.9$$b2022 000148274 591__ $$aENGINEERING, ELECTRICAL & ELECTRONIC$$b131 / 274 = 0.478$$c2022$$dQ2$$eT2 000148274 592__ $$a0.776$$b2022 000148274 593__ $$aApplied Mathematics$$c2022$$dQ2 000148274 593__ $$aArtificial Intelligence$$c2022$$dQ2 000148274 593__ $$aComputational Theory and Mathematics$$c2022$$dQ2 000148274 593__ $$aStatistics, Probability and Uncertainty$$c2022$$dQ2 000148274 593__ $$aElectrical and Electronic Engineering$$c2022$$dQ2 000148274 593__ $$aSignal Processing$$c2022$$dQ2 000148274 593__ $$aComputer Vision and Pattern Recognition$$c2022$$dQ2 000148274 594__ $$a4.5$$b2022 000148274 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/acceptedVersion 000148274 700__ $$0(orcid)0000-0002-3886-7748$$aOrtega, A.$$uUniversidad de Zaragoza 000148274 700__ $$aLópez-Espejo, I. 000148274 700__ $$0(orcid)0000-0001-9137-4013$$aLleida, E.$$uUniversidad de Zaragoza 000148274 7102_ $$15008$$2800$$aUniversidad de Zaragoza$$bDpto. Ingeniería Electrón.Com.$$cÁrea Teoría Señal y Comunicac. 000148274 773__ $$g127 (2022), 103536 [13 pp.]$$pDigit. signal process.$$tDIGITAL SIGNAL PROCESSING$$x1051-2004 000148274 8564_ $$s5486533$$uhttps://zaguan.unizar.es/record/148274/files/texto_completo.pdf$$yPostprint 000148274 8564_ $$s1275712$$uhttps://zaguan.unizar.es/record/148274/files/texto_completo.jpg?subformat=icon$$xicon$$yPostprint 000148274 909CO $$ooai:zaguan.unizar.es:148274$$particulos$$pdriver 000148274 951__ $$a2025-01-15-15:06:16 000148274 980__ $$aARTICLE