Design and implementation of a VoIP PBX integrated Vietnamese virtual assistant: a case study

IF 2.7 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS
Hoang Hai Son, Tran Anh Khoa, Thanh Phong Doan, Huu Khoa Tran, Duc Ngoc Minh Dang, Hoang Nam Nguyen
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引用次数: 0

Abstract

ABSTRACT As digitization is integrated into daily life, media are increasingly transferred over the Internet. Voice-over-Internet Protocol (VoIP), the most popular media transfer technology, is attracting many researchers and investments. The application of Artificial Intelligence (AI) technology into the Private Branch Exchange (PBX) has played a pivotal role in enhancing the customer experience and is able to unite employees in any company. One technology application used to optimize customer experience in a call centre is the use of an automatic PBX integrated with a Virtual Assistant (VA), which interacts directly with the PBX through voice and in multiple languages without any keystrokes. The Interactive Voice Response (IVR) module forwards the customer’s call to an operator or supports automatic processing. This solution can help businesses to handle thousands of calls per day with optimal performance, thus creating a customer care campaign that quickly reaches many users. A PBX integrated with Vietnamese Virtual Assistants (VVA) on an AI technology platform will also help businesses to cut down on operator costs with automated calls. Through comparison with a traditional PBX, this article analyzes, evaluates and optimizes an automatic PBX system with integrated VVA, thereby offering efficient solutions for interest companies.
基于VoIP PBX的越南虚拟助理的设计与实现
摘要随着数字化融入日常生活,媒体越来越多地通过互联网进行转移。VoIP是最流行的媒体传输技术,吸引了许多研究人员和投资。人工智能(AI)技术在专用交换机(PBX)中的应用在增强客户体验方面发挥了关键作用,能够团结任何公司的员工。一种用于优化呼叫中心客户体验的技术应用是使用与虚拟助理(VA)集成的自动PBX,该虚拟助理通过语音和多种语言直接与PBX交互,而无需任何按键。交互式语音响应(IVR)模块将客户的呼叫转发给操作员或支持自动处理。该解决方案可以帮助企业以最佳性能每天处理数千个电话,从而创建一个快速到达许多用户的客户关怀活动。在人工智能技术平台上与越南虚拟助理(VVA)集成的PBX也将帮助企业通过自动呼叫降低运营商成本。本文通过与传统PBX的比较,对一个集成了VVA的自动PBX系统进行了分析、评估和优化,从而为感兴趣的公司提供了有效的解决方案。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
7.50
自引率
0.00%
发文量
18
审稿时长
27 weeks
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