Speech Recognition Systems: Bridging Linguistics and Technology for Multilingual Communication

Abstract

Speech recognition systems have revolutionized human-computer interaction, enabling seamless communication across linguistic barriers. This article explores the intersection of linguistics and technology in the development of speech recognition systems, emphasizing their role in facilitating multilingual communication. By analyzing the evolution of automatic speech recognition (ASR) technology, we examine how linguistic theory, machine learning, and acoustic modeling converge to enhance system accuracy and usability across diverse languages. The paper also addresses challenges such as dialectal variations, noise interference, and the complexity of tonal languages, and discusses future directions for improving the inclusivity and adaptability of these systems. As speech recognition continues to advance, its potential to bridge global communication divides is becoming more apparent, promoting inclusivity in education, business, and global diplomacy.