148274 20250115160155.0 doi 10.1016/j.dsp.2022.103536 sideral 128724 ART-2022-128724 eng Prieto, S. Shouted and whispered speech compensation for speaker verification systems 2022 Nowadays, 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. Access copy available to the general public Unrestricted info:eu-repo/grantAgreement/ES/AEI/PDC2021-120846-C41 info:eu-repo/grantAgreement/ES/DGA/T36-20R info:eu-repo/grantAgreement/EC/H2020/101007666/EU/Exchanges for SPEech ReseArch aNd TechnOlogies/ESPERANTO This project has received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement No H2020 101007666-ESPERANTO info:eu-repo/grantAgreement/ES/MICINN-AEI/10.13039/501100011033 info:eu-repo/semantics/openAccess by-nc-nd http://creativecommons.org/licenses/by-nc-nd/3.0/es/ 2.9 2022 ENGINEERING, ELECTRICAL & ELECTRONIC 131 / 274 = 0.478 2022 Q2 T2 0.776 2022 Applied Mathematics 2022 Q2 Artificial Intelligence 2022 Q2 Computational Theory and Mathematics 2022 Q2 Statistics, Probability and Uncertainty 2022 Q2 Electrical and Electronic Engineering 2022 Q2 Signal Processing 2022 Q2 Computer Vision and Pattern Recognition 2022 Q2 4.5 2022 info:eu-repo/semantics/article info:eu-repo/semantics/acceptedVersion Ortega, A. Universidad de Zaragoza (orcid)0000-0002-3886-7748 López-Espejo, I. Lleida, E. Universidad de Zaragoza (orcid)0000-0001-9137-4013 5008 800 Universidad de Zaragoza Dpto. Ingeniería Electrón.Com. Área Teoría Señal y Comunicac. 127 (2022), 103536 [13 pp.] Digit. signal process. DIGITAL SIGNAL PROCESSING 1051-2004 5486533 http://zaguan.unizar.es/record/148274/files/texto_completo.pdf Postprint 1275712 http://zaguan.unizar.es/record/148274/files/texto_completo.jpg?subformat=icon icon Postprint oai:zaguan.unizar.es:148274 articulos driver 2025-01-15-15:06:16 ARTICLE