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Razvan Ene
Politehnica University of Bucharest, Bucharest, Romania
Civil Engineering, Building, Construction & Architecture Computer Science & IT Electrical & Electronics Engineering Engineering Mechanical Engineering

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Call-Center Virtual Assistant Using Natural Language Processing and Speech Recognition Andrei Vasilateanu; Razvan Ene
Journal of ICT, Design, Engineering and Technological Science Volume 2, Issue 2
Publisher : Journal of ICT, Design, Engineering and Technological Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33150/JITDETS-2.2.3

Abstract

We propose a proof of concept that uses state-of-the-art cloud-computing technologies to lay those basic functionalities that we have envisioned as our solution. One of our objectives is to test available NLP and cloud platforms. We follow a case-study approach to the research, in which the prototype is tested in different scenarios, including short and long conversations, and the results are examined and discussed. In more detail, we involve nine speakers, with different accents and speaking styles, in 85 scripts and 3 real-life conversations. Results are promising, with good accuracies for short conversations and limitations due to the lack of domain-specific knowledge bases applicable to the call-centre work. Call centre assistance is one of the many domains of activity that Artificial Intelligence could enable. Enter Customer Recommended Interaction Software (CRIS). The idea of a virtual agent that can offer assistance during a live call or troubleshooting procedure has great potential. It can be used to unlock a great extent of advantages. The virtual agent presents a dashboard to the employee, with relevant facts about the conversation presented in real-time. The dashboard contains elements such as call-category based on detected keywords and sentiment analysis.
Co-Authors Andrei Vasilateanu