AI-based morphed voice detection system for detecting fake voices

Artificial intelligence has enabled the creation of largely realistic synthetic voices through ways similar as voice morphing, textbook- to- speech systems, and deep generative models. While these technologies profit operations in virtual sidekicks, availability tools, and entertainment, they also raise serious pitfalls of abuse in fraud, impersonation, and misinformation. Detecting AI- generated or morphed voices has thus come a critical security challenge. This paper proposes an AI-Morphed Voice Detection System that leverages RGB spectrograms and a mongrel CNN – LSTM deep literacy model to classify speech as real or fake. The methodology involves preprocessing raw audio signals, converting them into spectrograms, rooting spectral features with CNN layers, and modeling temporal dependences with LSTM layers. The system was trained using stoked datasets and optimized with the Adam optimizer. Experimental results demonstrate promising performance with an overall ac- curacy of 69, showing strong perceptivity to morphed audio while relating challenges in balancing recall for real voices. This work contributes toward secure biometric authentication, forensic analysis, and digital communication systems by furnishing an effective frame for morphed voice discovery.