Objectives:
Crystal Clear aims to establish a consistent, practical way to measure and improve dialogue intelligibility across broadcast and streaming.
The project will define a unified metric for audibility by combining AI-based measurement tools with structured human listening assessments, creating a clear and reliable baseline for how dialogue intelligibility is evaluated.
It will then explore how this metric can be applied within production and distribution workflows to support more consistent and measurable improvements in dialogue quality.
Innovation and Collaboration:
Across the industry, dialogue intelligibility is already being measured using a range of tools, but these often produce inconsistent or non-comparable results, making it difficult to apply them reliably or consistently. There is a clear need to bring greater alignment to how audibility is assessed, by combining AI-based measurement with structured human listening assessments to establish a shared point of reference. Aligning technical outputs with how content is judged by listeners enables the development of a more consistent and practical framework for assessing dialogue clarity across different environments and use cases. This also creates a foundation for applying these approaches within production and distribution workflows, testing how they perform in different technical and operational environments.
This requires close collaboration between broadcasters, technology providers, and research partners. Broadcasters contribute real content and workflows, technology providers bring measurement and processing tools, and research partners support the design and validation of listening assessments. Working together in this way ensures that approaches are tested end-to-end, including within live production contexts, and are grounded in real-world conditions and capable of wider adoption across the industry.