Indian languages represent a highly multilingual and low-resource speech ecosystem, where the scarcity of high-quality parallel speech corpora significantly limits the development of speech-to-speech translation systems. Most existing approaches rely on cascaded pipelines that combine automatic speech recognition (ASR), machine translation (MT), and text-to-speech synthesis (TTS). While effective, these cascaded systems often suffer from cumulative error propagation, increased latency, and higher computational complexity, particularly in low-resource Indian languages. To address these challenges, my doctoral work proposes a novel sequence-to-sequence direct speech translation framework capable of translating speech from one Indian language to another without relying on intermediate text representations. Recent advances in deep learning, however, indicate that direct speech translation architectures can surpass conventional cascaded systems in both efficiency and translation quality, motivating the design of our fully end-to-end solution. We aim to release an initial dataset comprising at least 120,000 real speech samples within a 6–12 month timeframe.