Work place: Department of Electronics, Kristu Jayanti College, Bengaluru-560077, India
E-mail: itzamjad@gmail.com
Website:
Research Interests:
Biography
Amjad Hassan Khan M. K. received M.Sc. and M.Phil., from Bangalore University and Vinayaka Mission University respectively. currently pursuing Ph.D. from Srinivas University, Mangalore and working as assistant professor in Department of Electronics, Kristu Jayanti College (Autonomous). He has more than 16 years of teaching experience in Higher Education Institutes. He is member of Board of Examination (BOE) for Higher Education Institutes like Mount Carmel College, Bengaluru, Indian Academy, Bengaluru. His areas of interests are wireless communication systems, High Power devices, Biometrics. His research area is in voice biometrics.
By Amjad Hassan Khan M. K. P. S. Aithal
DOI: https://doi.org/10.5815/ijisa.2024.05.06, Pub. Date: 8 Oct. 2024
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.
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