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.
Cite
@article{arxiv.2412.09829,
title = {Speech-based Multimodel Pipeline for Vietnamese Services Quality Assessment},
author = {Quang-Anh N. D. and Minh-Duc Pham and Thai Kim Dinh},
journal= {arXiv preprint arXiv:2412.09829},
year = {2024}
}
Comments
I am writing to request the withdrawal of my preprint due to the discovery of significant inaccuracies in the results. These errors could mislead future research and applications, which compromises the integrity of my work. I believe withdrawing the paper is essential to uphold scientific standards and prevent the dissemination of misleading information. Thank you for your understanding