Közvetlenül a beszélt nyelvet és a hangszínt érti meg a Google AI

We’re moving beyond traditional text translation to build models that understand the world’s rich, living languages exactly as they are expressed. SVP, Research, Labs, Technology & Society Google is using AI to help people communicate in hundreds of languages, including those that were previously left out of technology. Instead of just translating text, they’re training AI to understand the emotion, tone, and slang people actually use in real life. They’re also working with local communities around the world to make sure their tools work for everyone, even those without reliable internet. This helps make sure that technology respects different cultures and helps people be understood on their own terms. 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. 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: Because the web disproportionately represents a few dominant languages, teaching AI to understand underrepresented languages required us to rethink how we gather data. The solution is local grassroots partnerships. This localized approach has driven three of our most ambitious open-data partnerships: We’re also building on our work prioritizing open-source language innovation through our new tool Language Explorer. It’s an interactive tool that visualizes LinguaMeta, the world’s largest open-source language data repository. Recognized by Fast Company for design innovation, it continuously maps more than 7,000 spoken, written, and signed languages. The impact of these innovations and partnerships is greatest when they reach the people who can turn new data and insights into meaningful change in their communities. Google.org-supported efforts, including the Centre for Digital Language Inclusion and AI Singapore’s Project Aquarium, are helping bring multilingual tools to farmers, healthcare workers, teachers, and other essential community members around the world. For more than 3 billion people 1 , reliable internet access is still out of reach. Technology is only truly accessible if it works where people live, including areas with limited or intermittent connectivity. To help address this, we developed TranslateGemma, a family of lightweight open translation models built from Gemini and trained across 55 languages. Because TranslateGemma runs efficiently on-device, high quality translation no