In the race to develop increasingly sophisticated AI models, the emphasis is often placed on scale: more data, larger models, and greater computing power. But what if the key to better performance isn’t more data, but better data?

Using text-to-speech (TTS) technology as a real-world use case, this session explores the critical relationship between training data quality and AI model performance, highlighting how high-quality datasets can produce stronger, more reliable results.

Drawing on practical experience, we’ll discuss the importance of prioritizing data quality over quantity and the broader implications for AI development. Attendees will gain insight into why data quality matters and how a quality-first approach can unlock new opportunities for more efficient, effective AI-driven media workflows