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

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 establishin...

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Bibliographic Details
Published in:Sensors (Basel, Switzerland) Switzerland), 2021-09, Vol.21 (18), p.6231
Main Authors: Krčadinac, Olja, Šošević, Uroš, Starčević, Dušan
Format: Article
Language:English
Subjects:
Access control
Accuracy
Algorithms
Biometrics
Communication
Discriminant analysis
e-commerce applications
Electronic commerce
Enrollments
identity management systems
Internet
Localization
Neural networks
speaker recognition
Speech
Speech recognition
Verification
Voice recognition
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Summary: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.
ISSN:1424-8220
1424-8220
DOI:10.3390/s21186231