A Review of Recent Machine Learning Approaches for Voice Authentication Systems

Abstract

Voice authentication systems are a comfortable way of protection since users do not need to remember passwords or carry identification cards. As a unique identifier for all individuals, voice is a practical tool to authenticate people into security services, including online banking and phonebased customer or computer services. Single-model voice authentication systems refer to voice recognition systems that utilize a single voice model to verify the identity of individuals based on their unique vocal characteristics, such as pitch, tone, and other speech patterns. For multi-model voice authentication systems, additional biometric factors like facial recognition or electroencephalogram data are included in the voice authentication process to enhance security. This paper reviews recent single-modal and multimodal voice authentication studies with an explanation of underlying feature extraction and classification methods. This paper also discusses security attacks on voice authentication systems, including random attacks, mimicry attacks, replay attacks, voice synthesizing attacks, counterfeit attacks, and hidden voice command attacks.