Speech-based Multimodel Pipeline for Vietnamese Services Quality Assessment

TL;DR

This paper introduces a deep-learning multi-modal pipeline that analyzes speech, interactions, and emotions to objectively assess Vietnamese service quality, surpassing traditional evaluation methods.

Contribution

It presents a novel multi-modal deep-learning approach specifically designed for Vietnamese service quality assessment, integrating speech, interaction, and emotional analysis.

Findings

Effective in analyzing speech and emotional content

Provides a comprehensive evaluation of customer interactions

Outperforms traditional assessment methods

Abstract

In the evolving landscape of customer service within the digital economy, traditional methods of service quality assessment have shown significant limitations, this research proposes a novel deep-learning approach to service quality assessment, focusing on the Vietnamese service sector. By leveraging a multi-modal pipeline that transcends traditional evaluation methods, the research addresses the limitations of conventional assessments by analyzing speech, speaker interactions and emotional content, offering a more comprehensive and objective means of understanding customer service interactions. This aims to provide organizations with a sophisticated tool for evaluating and improving service quality in the digital economy.