Deep Neural Architecture for Context-Aware Tamil Text-To-Speech Synthesis with Multilingual Scalability

The morphological abundance and high contextuality of Tamil, it is extremely challenging for Text-To-Speech (TTS) synthesis. This paper introduces a new Deep Neural Architecture for Context-Aware Tamil TTS Synthesis that improves intelligibility, naturalness, and multilingual flexibility through three major innovations: Multilingual Phoneme Embedding Network (MPEN) for better phoneme prediction from cross-lingual features, Prosody-Aware Transformer (PAT) for tone variation modeling and context-aware intonation, and Hybrid Spectrogram-Waveform Generator (HSWG) based on WaveNet and HiFi-Gan for high-fidelity speech synthesis. The resulting system achieves 98% intelligibility and 97.5% naturalness, surpassing state-of-the-art systems like Tacotron 2 and HiFi-Gan. The Mean Opinion Score (MOS) test verifies that the produced speech is extremely natural and human-like, and prosodic errors were decreased by 33% when compared with the older Tamil TTS models. Additionally, the model can be extended to other Indic languages and utilized in assistive technologies, AI-based voice assistants, real-time translation, and multilingual speech synthesis. The findings show that the system is efficient, scalable, and linguistically accurate, which is a valuable contribution to Tamil speech technology.