Classification of Emotions and Evaluation of Customer Satisfaction from Speech in Real World Acoustic Environments

Abstract

In today's world, customer service is becoming increasingly important in various industries, and providing excellent customer service is a key factor in ensuring business success. One aspect of customer service is the ability to recognize and respond to customers' emotions, as well as their satisfaction levels. However, this can be a challenging task, particularly when dealing with large volumes of customer interactions in different acoustic environments. This Paper focuses on the classification of emotions and evaluation of customer satisfaction from speech in real-world acoustic environments. The aim of the study is to develop an effective method for automatically recognizing emotions and determining customer satisfaction levels from speech data in different acoustic environments. To achieve this goal, a dataset consisting of customer service calls is used to train machine learning models that can accurately classify emotions and evaluate customer satisfaction. The models are evaluated using various metrics, such as accuracy, precision, recall, and F1-score, to assess their performance. The results show that the proposed method achieves high accuracy in both emotion classification and customer satisfaction evaluation tasks, indicating its potential for real-world applications in industries such as customer service, marketing, and healthcare.