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Evaluating the Performance of Speaker Recognition Solutions in E-Commerce Applications

Department for Information Technology, Faculty of Organizational Sciences, University of Belgrade, 11000 Belgrade, Serbia
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Academic Editors: Eui Chul Lee and Gi Pyo Nam
Sensors 2021, 21(18), 6231; https://doi.org/10.3390/s21186231
Received: 1 July 2021 / Revised: 5 August 2021 / Accepted: 13 September 2021 / Published: 17 September 2021
(This article belongs to the Special Issue Sensor-Based Biometrics Recognition and Processing)
Two important tasks in many e-commerce applications are identity verification of the user accessing the system and determining the level of rights that the user has for accessing and manipulating system’s resources. The performance of these tasks is directly dependent on the certainty of establishing the identity of the user. The main research focus of this paper is user identity verification approach based on voice recognition techniques. The paper presents research results connected to the usage of open-source speaker recognition technologies in e-commerce applications with an emphasis on evaluating the performance of the algorithms they use. Four open-source speaker recognition solutions (SPEAR, MARF, ALIZE, and HTK) have been evaluated in cases of mismatched conditions during training and recognition phases. In practice, mismatched conditions are influenced by various lengths of spoken sentences, different types of recording devices, and the usage of different languages in training and recognition phases. All tests conducted in this research were performed in laboratory conditions using the specially designed framework for multimodal biometrics. The obtained results show consistency with the findings of recent research which proves that i-vectors and solutions based on probabilistic linear discriminant analysis (PLDA) continue to be the dominant speaker recognition approaches for text-independent tasks. View Full-Text
Keywords: speaker recognition; biometrics; e-commerce applications; identity management systems speaker recognition; biometrics; e-commerce applications; identity management systems
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MDPI and ACS Style

Krčadinac, O.; Šošević, U.; Starčević, D. Evaluating the Performance of Speaker Recognition Solutions in E-Commerce Applications. Sensors 2021, 21, 6231. https://doi.org/10.3390/s21186231

AMA Style

Krčadinac O, Šošević U, Starčević D. Evaluating the Performance of Speaker Recognition Solutions in E-Commerce Applications. Sensors. 2021; 21(18):6231. https://doi.org/10.3390/s21186231

Chicago/Turabian Style

Krčadinac, Olja, Uroš Šošević, and Dušan Starčević. 2021. "Evaluating the Performance of Speaker Recognition Solutions in E-Commerce Applications" Sensors 21, no. 18: 6231. https://doi.org/10.3390/s21186231

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