Multi-Label Classification of Voice Calls from Power Plant Operation Centers
Frederico Dias Souza, Camila Barbosa, João Francisco Gonçalves, Victor Furtado, Amanda Amaro, Felipe Pena, Ranielly C. Reis, Adrisson C. Floriano, Reginaldo de Oliveira · 2021
This paper presents a machine learning-based pipeline to classify, in six labels, voice calls from power plant operation centers from ENGIE Brasil Energia (private power producer). The pipeline consists of a customized speech-to-text model from Amazon Web Services (AWS) followed by a multi-label text classification model. Our experiments showed how to leverage the performance of Amazon Transcribe with a custom vocabulary, as well as the predictive performance for different tree-based machine learning models. The work aims to facilitate the audit of internal actions and increase operational efficiency from the post-operation activities from ENGIE Brasil Energia (EBE). We achieved great predictive results, high accuracy, and Fl-score for all six labels.