Natural Language Processing (NLP) based Phonetic Insights for Improving Voice Recognition and Synthesis

Voice analysis in Natural Language Processing (NLP) involves the processing of spoken language to develop applications such as speech recognition, synthesis, and speaker identification. This survey explores the phonetic intricacies of Tamil and other Dravidian languages, examining their impact on NLP. The extensive phonetic inventory of the Tamil Language is characterized by its complex vowel system, retroflex consonants, and the distinction between aspirated and unaspirated stops, which presents unique challenges and opportunities for computational linguistics. Phonetics helps to process the human language both accurately and naturally and it works as a bridge between the spoken and written communication forms. This paper emphasizes the necessity of developing specialized phonetic modeling techniques and robust algorithms. Additionally, it highlights the importance of creating comprehensive phonetic datasets and leveraging advancements in machine learning and deep learning to improve NLP tasks such as speech recognition and synthesis. A brief comparison on algorithms and applications associated with voice-assisted technologies. By advocating for interdisciplinary collaboration among linguists, computer scientists, and language technologists, this survey aims to inspire further research and innovation in processing Tamil and other Dravidian languages, ultimately contributing to more inclusive and efficient NLP systems for these linguistically rich languages.