International Journal of Innovative Research in Computer and Communication Engineering

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TITLE AI-Enabled Conversational IVR System Using Generative AI for Intelligent Customer Interaction
ABSTRACT A Conversational IVR (CI-IVR) system designed to improve traditional telephony customer service by enabling more natural, personalized automated voice interactions. Key features include integration of large language models, multilingual transformer-based natural language understanding, and neural speech synthesis. The system is cloud-native and modular, with components for noise-robust speech recognition, intent classification, generative response synthesis, sentiment analysis, text-to-speech, and omnichannel dispatch. In tests involving 5,000 calls and simulated 18,400 call interactions, the system achieved 94.7% intent detection accuracy, 870 ms average latency, and more than doubled call containment compared to traditional DTMF systems. It also succeeded in 88.2% of multilingual queries with code-switching across six Indian languages. Overall, the CI-IVR framework presents a robust, scalable, and linguistically adaptive solution for next-generation automated contact center services. If you want, I can help summarize, explain specific parts, or assist with related tasks.
AUTHOR MANJULA P, SANTHOSH MR, SHREYA N GOWDA, SMRUTI S, SUHA NOORAIN HI Assistant Professor,Department of Computer Science and Engineering, Jain Institute of Technology, Davangere, Karnataka, India Department of Computer Science and Engineering, Jain Institute of Technology, Davangere, Karnataka, India
VOLUME 183
DOI DOI: 10.15680/IJIRCCE.2026.1404113
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KEYWORDS
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