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Abstract

Deepfake voice technologies have emerged as a significant advancement in artificial intelligence, particularly within speech synthesis and voice cloning. Using deep learning models such as Generative Adversarial Networks (GANs) and autoencoders, these systems can generate highly realistic synthetic voices that mimic human speech. While beneficial for entertainment and accessibility, deepfake voices also pose major risks in misinformation, identity theft, and cybercrime. This paper explores both the generation techniques and detection strategies for deepfake voices, focusing on neural network–based approaches for voice authentication and synthetic speech recognition. It also highlights the ethical and legal implications of deepfake usage, with emphasis on consent, digital trust, and privacy. By critically analyzing recent trends and proposing a framework for detection, the study aims to support the development of robust defenses against malicious voice manipulation.