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Paris partners with accessibility developer Ava to create a EUR 2m speech-to-speech translation app expected to help metro agents handle communications during the Olympics.
KAIST and Google DeepMind researchers propose training a single model using Unit-to-Unit Translation to achieve seamless cross-language communication.
In just three months, universities and tech companies released over two dozen papers on speech-to-speech translation (S2ST), highlighting continuing trends, new directions in research.
Meta has released SpeechMatrix, a large-scale mined corpus of multilingual speech-to-speech translations.
Zoom business users can now have their meetings transcribed, translated, and captioned in real time into and out of English from any of 10 languages.
Meta eliminates text generation in speech to speech translation; shares code and research with the public.
Part of the billions of dollars going into the Metaverse will undoubtedly go into these two areas of machine translation. Meta AI reveals its MT roadmap and the people working on it.
As the language industry turns to speech-to-speech translation, Facebook AI partners with “AI community” Hugging Face to release speech-to-text translation models for four languages.
Zoom call add-ons planned for beta release in September 2021. Transcription available in 30 languages, and translation, in 12, by year-end 2022.
Facebook sees textless natural language processing (NLP) as rendering automatic speech translation (ASR) obsolete by working in true end-to-end fashion: from speech input to speech output.
Google researchers say Translatotron 2 outperforms original in terms of speech-to-speech translation quality and naturalness. Plus the new version prevents users from generating speech for deepfakes.
Zoom acquires German simultaneous speech translation provider, Kites, to provide multilingual speech translation to Zoom users; explores opening R&D center in Germany.
European Live Translator (ELITR) takes speech-to-speech translation one step further, backed by the academe, private sector, and government.
Google researchers approach speech-to-speech translation a little differently in a proof-of-concept that completely takes away reference text, but underperforms compared to existing models.
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