Papercup blog archive

Voice actors worry that AI will take their jobs. Here's why we don't think so.

Introducing XLPT: The first wholly automated system to translate emotion across languages!

Are AI dubbing and traditional dubbing sworn enemies or complementary methods serving growing global content demand?

Here's how they're made, by whom, for what, and how Papercup's sound so lifelike.

Here's how voice clones are made, when they should be used and why.

Here's what machine learning researchers must tackle to achieve C-3PO level synthetic speech.

A lookback at 2023 – the year generative AI exploded, major shifts in the digital and streaming worlds, and some key Papercup milestones.

If dubbed content fails on translation accuracy, emotional depth and clarity, it won't engage audiences.

CGI was received with trepidation, now its creative capabilities have been embraced; Generative AI is treading a similar path, but is its future as promising?

What is a large language model? Training data? What's a human-in-the-loop? Answers to all of the above in this glossary of key AI terms.

Papercup's Head of Product imparts learnings on utillizing machine learning capabilities to develop valuable products with real-world use cases.

The ongoing translator shortage is lengthening timelines and production costs, and traditional studio dubbing can't localize content at scale.