AI-Assisted Translation, Meeting and Customer Service Approach: Interactive Reply Assistance

AI-Assisted Translation, Meeting and Customer Service Approach: Interactive Reply Assistance

Dr. Alparslan KAPLAN, Prof. Dr. Eren ÖZCEYLAN

Abstract

The use of artificial intelligence technologies in both business and daily life is increasing day by day. With the help of machine learning-based algorithms, instant language translations (in the original tone of voice and lip synchronization) and 24/7 customer service have become more feasible. This can eliminate traditional customer service delays, initiate instant responses, translate into any language and deliver all this seamlessly.

In this study, an artificial intelligence assistant that serves in three phases is developed. In the first phase, the assistant prepares online meeting notes as executive summaries. In the second stage, these notes are translated into the desired language as audio and video in a tone and lip synchronized manner. In the last stage, the ASR (Automatic Speech Recognition) model is used to recognize the sentences, requests and questions of the customers during the conversation phase.

I. Introduction

Recently, there has been an increased interest in artificial intelligence (AI) in various fields and sectors. AI tools have been created with a wide range of applications to assist or replace humans in the fulfilment of various tasks. In the service sector, AI has made significant progress as a collaborative tool in performing tasks, potentially transforming the way companies provide services to their customers.

Particularly in online meetings, which came into our lives during and after COVID-19, it is an important task to take notes of what is discussed and provide a summary to managers. On the other hand, translating these notes or videos into the desired language in a lip- and tone-coherent manner is a necessity in the international arena. Finally, the use of artificial intelligence assistants for customer care services in different service sectors (banking, health, etc.) is important for uninterrupted service understanding.

This study proposes a three-stage model within a holistic approach. In the first stage, an AI-powered assistant participates in online meetings as an avatar, taking the necessary notes and making executive summaries. In the second stage, communication is provided with both voice and lip synchronization by translating into the desired language. In the last stage, the AI assistant serves as a call center and collects relevant data from customers or stakeholders.

II. Proposed Models

In the literature, there is no study that brings together the three different processes mentioned above in an integrated manner. Therefore, the literature on each application area is investigated.

A. Virtual Meeting Assistant

The speech recognition and translation part of the application is the part of the application where an Automatic Speech Recognition (ASR) model is used to recognize the sentences, requests and questions of the customers during the conversation. The automatic speech recognition model was created by training it with over ten thousand data using deep learning algorithms.

The data set required to train the automatic speech recognition model was created by collecting words from thirty-seven different languages. Thus, the model has the capacity to automatically detect and respond to speech in thirty-seven different languages.

Literature Review

In one of the related studies in the literature, a Real-Time Speech-to-Speech Translation system for Virtual Meetings is developed by Karunya et al. [1]. Their model captures speech in one language, providing clear and understandable translations in real-time during virtual meetings integrating Automatic Speech Recognition (ASR), Machine Translation (MT), and Text-to-Speech (TTS) components.

In a similar study, Wyawahare et al. [2] develop an automated system that can generate comprehensive summaries of meetings by analysing both textual and audio data. Leveraging advanced Natural Language Processing (NLP) and audio processing techniques, the model adeptly extracts key points, action items and relevant information from meeting transcripts and audio recordings eliminating the need for laborious manual review.

Results and Contributions

This research makes an important contribution to the field of applied artificial intelligence by integrating three advanced technologies into a single integrated system. The developed system is capable of handling complex challenges in the modern business environment, especially in international contexts where the need for real-time translation and multilingual communication is paramount.

Initial results indicate that the system can significantly improve meeting efficiency and reduce the need for human intervention in many customer service tasks, leading to cost savings and improved service quality. The system's ability to maintain context across different phases of interaction makes it particularly valuable for complex business scenarios.