![]() To do this, we developed a variety of methods, such as using speech-to-unit translation to translate input speech to a sequence of acoustic sounds, and generated waveforms from them or rely on text from a related language, in this case Mandarin. So, we focused on speech-to-speech translation. However, since primarily oral languages don’t have standard written forms, producing transcribed text as the translation output doesn’t work. Many speech translation systems rely on transcriptions. ![]() We believe spoken communication can bring people together wherever they are located - even in the metaverse. The translation system is part of our Universal Speech Translator project, which is developing new AI methods that we hope will eventually allow real-time speech-to-speech translation across many languages. We’re open-sourcing our Hokkien translation models, evaluation datasets and research papers so that others can reproduce and build on our work. ![]() To address this challenge, we’ve built the first AI-powered speech-to-speech translation system for Hokkien, a primarily oral language that’s widely spoken within the Chinese diaspora but lacks a standard written form. This makes it impossible to build machine translation tools using standard techniques, which require large amounts of written text in order to train an AI model. AI-powered speech translation has mainly focused on written languages, yet nearly 3,500 living languages are primarily spoken and don’t have a widely used writing system.
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