Part 5/13:
The speaker introduces their own foundational model, Mahat TTS, which was developed from scratch to handle numerous languages with high fidelity. They emphasize the importance of diverse datasets—not just in quantity but in cultural and linguistic variety—to build models capable of nuanced, emotion-aware speech synthesis.
They discuss the progression from short-context models (covering around 10 seconds of speech) to long-context models capable of maintaining coherence over extended speech, such as audiobooks or emotional narration. This transition significantly improves content consistency and naturalness.