Identification of Customer Through Voice Biometric System in Call Centres
Автор: Amjad Hassan Khan M.K., P.S. Aithal
Журнал: International Journal of Intelligent Systems and Applications @ijisa
Статья в выпуске: 5 vol.16, 2024 года.
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In recent times, there has been a growing emphasis on adjusting communication strategies to foster strong customer relationships. This shift is driven by intensified competition, market maturation, and swift advancements in business technology. Consequently, companies have established call centers to efficiently handle customer support and fulfil customer inquiries. A pivotal aspect of enhancing service quality within these call centers involves accurately identifying customers during their interactions. The primary objective of this study is to introduce a methodology for identifying customers within call centers by analysing their voice characteristics. Voice authentication (VA) has gained prominence in critical security operations, including banking transactions and conversations within call centers. The susceptibility of automatic speaker verification systems (ASVs) to deceptive spoofing attacks has prompted the development of countermeasures (CMs). These countermeasures are designed to differentiate between authentic and fabricated speech. ASVs and CMs collectively constitute contemporary VA systems, positioned as robust access control mechanisms. To achieve this goal, various customer identification systems within call centers have been examined, along with an analysis of audio signal attributes. Ultimately, the manuscript presents a novel approach to customer identification through voice biometrics. Notably, this method excels in recognizing customers even when provided with limited voice data. Empirical findings demonstrate that the suggested speaker identity confirmation method outperforms alternative techniques utilizing different algorithms, exhibiting a higher recognition rate. The present research work is based on two important perspectives of the call centres: a. call center agents experience and b. customer experience. The data collected separately from customers and agents for understanding the effective usage of voice biometric system in call centres. The data represented and satisfies the effectiveness of voice biometric system from both the perspectives. From the data it is also cleared that, the implementation of voice biometric system in call centres still have long way to go but will be a major technological change for the industries worldwide.
Voice Biometric, Call Centers, Customer Relation Management, Voice Authentication
Короткий адрес: https://sciup.org/15019519
IDR: 15019519 | DOI: 10.5815/ijisa.2024.05.06
Список литературы Identification of Customer Through Voice Biometric System in Call Centres
- De Keyser, A., Bart, Y., Gu, X., Liu, S. Q., Robinson, S. G., & Kannan, P. K. Opportunities and challenges of using biometrics for business: Developing a research agenda. Journal of Business Research, 136(1): 52-62, 2021.
- Rabea Kurdi, Fawzia Hersi, Sara Bahagari, Mohammed Kaosar, S. M. Qaisar, A. Subasi. A Mobile Fingerprint Authentication in Saudi Arabian Call Centers, International Conference on Electrical and Computing Technologies and Applications (ICECTA), 2017
- Nuance provides voice biometric technologies for Manulife contact centers, Retrieved November 21, 2015, from http://www.biometricupdate.com/201509/nuance-provides-voicebiometric-technologies-for-manulife-contact-center, 2015.
- Ahmad, S. M. S., Ali, B. M., & Adnan, W. A. W. Technical issues and challenges of biometric applications as access control tools of information security. International journal of innovative computing, information and control, 8(11): 7983-7999, 2012.
- Kristof Coussement, Dirk Van den Poel. Integrating the voice of customers through call center emails into a decision support system for churn prediction, Information & Management, 45(1):164–174, 2008.
- W. Reinartz, V. Kumar. The impact of customer relationship characteristics on profitable lifetime duration, Journal of Marketing, 67(1):77–99, 2003.
- S.L. Pan, J.N. Lee. Using e-CRM for a unified view of the customer, Communications of ACM, 46(4):95–99, 2003.
- T. Toda, A. Black, and K. Tokuda. Voice conversion based on maximum-likelihood estimation of spectral parameter trajectory, IEEE Transactions on Audio, Speech, and Language Processing, 15(1):2222–2235, 2007.
- Andre Kassis, Urs Hengartner. Breaking Security-Critical Voice Authentication, 2023 IEEE Symposium on Security and Privacy (SP), 2023.
- H. Abdullah, K. Warren, V. Bindschaedler, N. Papernot, and P. Traynor. The faults in our ASRs: An overview of attacks against automatic speech recognition and speaker identification systems, in IEEE S&P, Oakland, 2020.
- Monireh Hosseini, Elnaz Nasirzadeh. Customer identification through voice biometric index at call centers using learning algorithms, International Journal of Economics, Commerce and Management, 3(5):1519-1535, 2015.
- Benesty, J., Sondhi, M. M. & Huang, Y. In: Springer Handbook of Speech Processing. s.l.:Springer, 2008.
- Jain, A., Hong, L. & Pankanti, S. Biometric Identification. Communications of the ACM, 43(2):90-98, 2000.
- Magboub, H. M., Ali, N., Osman, M. A. & Alfandi, S. A. Multimedia speech compression techniques, s.l., IEEE, 9-11, 2011.
- Sahoo, S. K., Choubisa, T. & Prasanna, S. M. Multimodal Biometric Person Authentication: A Review. IETE Technical Review, 29(1): 54-75, 2012.
- SoonHoo So. An Empirical Analysis on the operational Efficiency of CRM call centers in Korea, International Journal of Computer Science, 7(12):171-178, 2007.
- Joaquín González-Rodríguez, Doroteo Torre Toledano & Javier Ortega-García. Handbook of Biometrics, pages 151–170, 2008.
- Hicham Atassi; Zdenek Smékal. Automatic identification of successful phone calls in call centers based on dialogue analysis, 5th IEEE Conference on Cognitive Infocommunications (CogInfoCom), Vietri sul Mare, Italy, pages 425-429, 2014.
- Andrew Boles; Paul Rad. Voice biometrics: Deep learning-based voiceprint authentication system, 12th System of Systems Engineering Conference (SoSE), Waikoloa, HI, USA, pages 1-6, 2017.
- Kao, C. Y., & Chueh, H. E. Voice Response Questionnaire System for Speaker Recognition Using Biometric Authentication Interface. Intelligent Automation & Soft Computing, 35(1), 2023.
- Ranjan, Shashi; Mahesh, P. K. Voiceprint Authentication System to Securely Verify and Protect Personal Identity, Grenze International Journal of Engineering & Technology (GIJET), 3(3): 241-243, 2017.
- B. Yan, R. Zhang and Z. Yan. VoiceSketch: a Privacy-Preserving Voiceprint Authentication System, IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom), Wuhan, China, pages 623-630, 2022.