Recognition of Human Emotions by Voice in the Fight against Telephone Fraud

Abstract

Advances in communication technologies have made communication between people more accessible. In the era of information technology, information exchange has become very simple and fast. However, personal and confidential information may be available on the Internet. For example, voice phishing is actively used by intruders. The harm from phishing is a serious problem all over the world, and its frequency is growing. Communication systems are vulnerable and can be easily hacked by attackers using social engineering attacks. These attacks are aimed at tricking people or businesses into performing actions that benefit attackers, or providing them with confidential data. This article explores the usefulness of applying various approaches to training to solve the problem of fraud detection in telecommunications. A person's voice contains various parameters that convey information such as emotions, gender, attitude, health and personality. Speaker recognition technologies have wide areas of application, in particular countering telephone fraud. Emotion recognition is becoming an increasingly relevant technology as well with the development of voice assistant systems. One of the goals of the study is to determine the user model that best identifies fraud cases. Machine learning provides effective technologies for fraud detection and is successfully used to detect such actions as phishing, cyberbullying, and telecommunications fraud.