SPEECH RECOGNITION AND PHONETIC VARIATION:UNDERSTANDING THE IMPACT OF ACCENTS AND DIALECTS ON AI-BASED SPEECH SYSTEMS

Abstract

The paper examines the limitations and developments in the speech recognition system driven by AI especially in dealing with phonetic variation in the English language. Phonetic diversity such as accents, dialects, and sociolects become serious obstacles to the precision and inclusivity of the AI systems. Though speech recognition has come a long way, it continues to be undermined to large extents by training data that is biased towards standard accents causing much of non-native speaker, and regional variation, and marginalized groups to lack representation, and through lack of training data. The paper analyses the ethical, cultural, and technical concerns that might be caused by these challenges with special focus on linguistic bias, privacy issues, and cultural sensitivity in the implementation of AI. The paper further explores new developments in multilingual and multidialectal training, in deep learning and adapt-on-the-fly in order to enhance the capacity of speech recognition to accommodate various phonetic variation. It also highlights the fact that virtual assistants, healthcare, education, and business are viable developments that can be applied in practical settings by analyzing virtual assistant case studies. The paper also proposes the more inclusive AI systems it concludes with providing better representation, privacy, and ethical design to ensure that AI-derived speech recognition systems are available, precise, and culturally inclined to all users.