There are growing implications surrounding generative AI in the speech domain that enable voice cloning and real-time voice conversion from one individual to another. This technology poses a significant ethical threat and could lead to breaches of privacy and misrepresentation, thus there is an urgent need for real-time detection of AI-generated speech for DeepFake Voice Conversion. To address the above emerging issues, the DEEP-VOICE dataset is generated in this study, comprised of real human speech from eight well-known figures and their speech converted to one another using Retrieval-based Voice Conversion. Presenting as a binary classification problem of whether the speech is real or AI-generated, statistical analysis of temporal audio features through t-testing reveals that there are significantly different distributions. Hyperparameter optimisation is implemented for machine learning models to identify the source of speech. Following the training of 208 individual machine learning models over 10-fold cross validation, it is found that the Extreme Gradient Boosting model can achieve an average classification accuracy of 99.3% and can classify speech in real-time, at around 0.004 milliseconds given one second of speech. All data generated for this study is released publicly for future research on AI speech detection.

Could you tell if you were listening to an AI-generated voice? Even when people know they may be listening to AI-generated speech, it is still difficult for both English and Mandarin speakers to reliably detect a deepfake voice. That means billions of people who understand the world's most spoken languages are potentially at risk when exposed to deepfake scams or misinformation. Kimberly Mai at University College London and her colleagues challenged more than 500 people to identify speech deepfakes among multiple audio clips. Some clips contained the authentic voice of a female speaker reading generic sentences in either English or Mandarin, while others were deepfakes created by generative AIs trained on female voices.