Oyucu, SaadinÇelimli, Derya Betül ÜnsalAksöz, Ahmet2025-05-042025-05-042024979-835035442-3https://doi.org/10.1109/ISMSIT63511.2024.10757293https://hdl.handle.net/20.500.12418/350168th International Symposium on Multidisciplinary Studies and Innovative Technologies, ISMSIT 2024 -- 7 November 2024 through 9 November 2024 -- Ankara -- 204563This study focuses on developing and evaluating a hybrid Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM) based deep learning model for detecting fake voice recordings. The proposed model addresses the critical issue of artificial intelligence-generated speech mimicking human voices, which can potentially be used for malicious purposes, thereby endangering individuals' privacy and safety. A comprehensive dataset comprising 5,889 real and 5,889 fake voice samples was utilized for this research. The dataset underwent rigorous preprocessing, including segmentation into fixed-length windows and normalization. The hybrid CNN-LSTM model was then trained and validated systematically involving exploratory data analysis and extensive hyperparameter tuning. The experimental results demonstrated that the proposed model achieved an accuracy of 99.2%, an F1 score of 99.2%, a recall of 99.4%, and a precision of 99.0%, indicating its robust performance in distinguishing between real and fake voices. The findings underscore the potential of the hybrid CNN-LSTM model as a powerful tool for safeguarding digital communications against the growing threat of fake voices. © 2024 IEEE.en10.1109/ISMSIT63511.2024.10757293info:eu-repo/semantics/closedAccessaudio forensicsconvolutional neural network (CNN)fake voice detectionlong short-term memory (LSTM)Conference Object2-s2.0-85213320665N/A