Cross-Lingual Speech-to-Speech Translation: A Generative AI Approach for Smooth Code Switching between Tamil and Dravidian Languages

Cross-lingual speech-to-speech translation from Tamil to other Dravidian languages is a critical undertaking that necessitates the use of modern natural language processing (NLP) techniques. This research describes a revolutionary generative AI-based approach for smooth code-switching between Tamil and Dravidian Languages. To generate high-quality translations, the proposed system employs an encoder-decoder architecture and an attention mechanism. The system is also trained on a vast dataset of parallel sentences in Tamil and other Indian languages, allowing it to grasp the intricacies of each language and create correct translations.Compared to traditional statistical machine translation approaches, the suggested system has significant advantages. For starters, it can manage multi-language code-switching, allowing users to transition between Tamil and other Indian languages without losing context. Second, even when working with complicated sentence structures and idiomatic idioms, it delivers fluent and cross-lingual natural-sounding translations. Finally, the system is extremely scalable and easily integrated into a variety of business-to-business situations, allowing for effective communication across linguistic boundaries.Overall, the proposed system offers a substantial advancement in cross-lingual speech-to-speech translation technology for low-resource Dravidian Languages, with potential applications in customer service, e-commerce, and education. Because of its capacity to handle code-switching and generate high-quality translations, it is a suitable tool for enterprises that operate in multilingual environments.