AI for everyone in every language

Today, our technologies and products power everyday interactions in more than 300 languages, spoken by more than 7 billion people — representing 86% of the global population. Reaching this milestone is meaningful, but it also underscores work that is critical to our mission. For decades, technology has worked best for a handful of dominant languages, leaving thousands of living languages and dialects poorly represented or absent altogether from the digital world.

When we launched Google Translate in 2006, our goal was simple: to break down the barriers between languages. Advances in AI have helped us bring that vision to more people, expanding Translate from a handful of languages to more than 250 today. But translating text isn’t enough. Technology needs to understand how people actually communicate in the real world. So we focus our research and development on building systems that honor cultural nuance and the richness of human language, enabling everyone to participate and be understood on their own terms.

Here’s what that work looks like in practice.

Going from text to true understanding

Historically, speech recognition systems followed a rigid, multi-step process: transcribing audio into text, processing that text, and then synthesizing it back into audio. While functional, this pipeline strips away the richest parts of human communication: tone, pacing, emotion, and context.

People don't speak in perfectly neat, grammatical sentences. We laugh, overlap, hesitate, and weave multiple languages together mid-sentence, like when we speak Spanglish or Hinglish.

To capture this, we moved beyond text transcripts to native audio intelligence — training models like Gemini to process audio directly as is, while also grasping both sound and intent. These efforts include: