We’re moving beyond traditional text translation to build models that understand the world’s rich, living languages exactly as they are expressed.
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 respec…
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 ourmission. 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 th…
When we launchedGoogle Translatein 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 a…
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 toolLanguage Explorer. It’s an interactive tool that visualizes LinguaMeta, the world’s largest open-source language data repository. Recognized byFast 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 theCentre for Digital Language Inclusionand AI Singapore’sProject Aquarium, are helping bring multilingual tools to farmers, healthcare workers, teachers, and other essential community members around the world.
For more than 3 billion people1, 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 developedTranslateGemma, 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 longer requires a connection to the cloud or the internet.
Still, running powerful AI models requires capable hardware, which excludes the hundreds of millions of people still using feature phones in low-resource regions. To bridge this divide, we’re supporting organizations likeViamoto power “Ask Viamo Anything” (AVA), a voice AI assistant that brings the power of Gemini to standard feature phones. Viamo has successfullypiloted AVA in Rwandawith its existing interactive voice response users, and the ser…
Language isn’t just about regional dialects or vocabulary. It’s also about the many other ways people communicate. Conventional speech tools frequently fail people with non-standard speech, making them adapt to the technology rather than the other way around.
We’re working to change that by designing for accessibility from the ground up, for example, withSign Language-to-Text (SL2T). Trained across 50+ sign languages, SL2T powers sign-to-text dictation in Gboard and Live Transcribe on Pixel 11, starting with American Sign Language (ASL) to English. This is an important first step toward making our products more accessible to the 70 million people worldwide who rely on sign language to communicate.
Details matter, certainly in everyday tools like navigation. When a navigation app mispronounces a town or street name, it doesn’t just cause confusion, it can overlook the cultural heritage of the place.
We believe cultural context should be part of how language technology is built, and that means working directly with local communities. For example, in New Zealand, weworked with Māori language experts to improve the place name pronunciation in Google Maps, helping make the experience more accurate and genuinely local. Incorporating culturally authentic pronunciations directly into our text-to-speech models ensures technology reflects the languag…
One essential lesson we’ve learned over 20 years of AI language research and development is that technology should never narrow the spectrum of human expression — it should expand it.
Today, our language technologies are already embedded across our core ecosystem, connecting more than five billion people across nine platforms, including Search, Android, Chrome, YouTube, and Google Play. But scale is only part of the story. The bigger goal is depth and richness — building systems that grasp context, respect culture, and celebrate the many ways people communicate.
As we expand our use ofAI to address some of society’s biggest opportunities, we’ll continue to work closely with local communities to build technology that helps more people communicate, participate, and be understood on their own terms.
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John Koetsier, “3.4 Billion People Don’t Have Access to Mobile Internet, Costing the Global Economy $3 Trillion,” Forbes, February 18, 2026.