SPEECH RECOGNITION SYSTEMS: FROM HUMAN PHONETICS TO ARTIFICIAL INTELLIGENCE

Abstract

Speech recognition systems have undergone remarkable evolution, progressing from early phonetic-based approaches to highly sophisticated artificial intelligence (AI)-driven frameworks. This paper explores the historical development, theoretical underpinnings, and practical applications of these systems, emphasizing their transformative role in modern technology. Beginning with human phonetics, the foundation of early speech recognition models, the research highlights the integration of machine learning (ML) and deep learning (DL) to achieve unprecedented accuracy and versatility. By analyzing cutting- edge systems such as Wav2Vec 2.0 and their impact across industries, this paper provides insights into current methodologies, challenges, and potential future directions. Ethical considerations and the implications of linguistic diversity are also discussed, offering a comprehensive overview of the field's trajectory.