Forensics refers to scientific investigations of criminal activities. In the digital forensic domain, phonetics is gaining attention from researchers. In the past, it was not as highlighted as images or videos. Considering the importance of voice as a powerful means of communication in the digital world, it has become an interesting topic for research. For instance, speaker identity verification is crucial in banks, multimedia, and biometrics. However, voice can be easily tampered with or disguised, creating social panic or affecting the speaker’s identity. This paper provides a comprehensive overview of voice modification in speaker identification, making a valuable contribution to criminal investigations. It covers various topics, including voice disguise methods, analysis techniques used to detect disguises and forensic approaches for solving criminal cases in phonetics. The paper aims to understand these interrelated fields better and identify potential avenues for future research and development. It achieves this by providing a detailed review of the methodology used to identify disguised voices in forensic investigations and presenting a tabular overview of the developments in this area over the years.

Deepfake audio refers to the generation of voice recordings using deep neural networks that replicate a specific individual’s voice, often for deceptive or fraud purposes. Although this has been an area of research for quite some time, deepfakes still pose substantial challenges for reliable true speaker authentication. To address the issue, we propose AudioFakeNet, a hybrid deep learning architecture that use Convolutional Neural Networks (CNNs) along with Long Short-Term Memory (LSTM) units, and Multi-Head Attention (MHA) mechanisms for robust deepfake detection. CNN extracts spatial and spectral features, LSTM captures temporal dependencies, and MHA enhances to focus on informative audio segments. The model is trained using Mel-Frequency Cepstral Coefficients (MFCCs) from the publicly available dataset and was validated on self-collected dataset, ensuring reproducibility. Performance comparisons with state-of-the-art machine learning and deep learning models show that our proposed AudioFakeNet achieves higher accuracy, better generalization, and lower Equal Error Rate (EER). Its modular design allows for broader adaptability in fake-audio detection tasks, offering significant potential across diverse speech synthesis applications.