Deepfake speech recognition: evaluating accuracy and efficiency in detection algorithms

Abstract

Artificial intelligence (AI), in recent times, is experiencing explosive growth, which comes with improvement and challenges in ethics. In this case, most of the problems raised by deepfake technologies revolve around the issue of changing or producing any sound using sophisticated technology for purposes of mimicking someone’s voice and involves people ranging from ordinary citizens to public figures thus creating a danger to security and privacy. This study mainly concentrates on the important task of recognizing deepfake speech and evaluating the performance of selected speech recognition models in terms of their accuracy and efficiency. Current deepfake speech detection methodologies are explained and their strengths and weaknesses are discussed. The focus of this research work is therefore geared towards designing robust countermeasures against harmful use of deep fake voice technology which enhances trust in electronic communication.