Published Paper Details:

The emergence of voice cloning technology has brought about several difficulties and the possibility of abuse in a few contexts, hence strong detection systems are required. This overview paper offers a thorough analysis of contemporary and conventional methods for identifying voice clones. A thorough examination of the advantages and disadvantages of conventional techniques, including Mel-Frequency Cepstral Coefficients (MFCC) in conjunction with similarity measurements, is conducted. Furthermore, current methods utilizing machine learning (ML) and artificial intelligence (AI) models are reviewed, emphasizing their versatility and efficacy in recognising artificial voices. A comparative examination of these approaches is included in the survey, and their accuracy, efficiency, and scalability are assessed. The purpose of this analysis is to clarify the status of voice cloning detection, point out areas of research deficiency, and make recommendations for future improvements to detection capabilities. The survey's findings are meant to guide the creation of more sophisticated and trustworthy detection systems, which will ultimately help to protect audio communications' authenticity.