The TC-STAR project is envisaged as a long-term effort to advance research in all core technologies for Speech-to-Speech Translation (SST). SST technology is a combination of Automatic Speech Recognition (ASR), Spoken Language Translation (SLT) and Text to Speech (TTS) (speech synthesis). The objectives of the project are ambitious: making a breakthrough in SST that significantly reduces the gap between human and machine translation performance. The project targets a selection of unconstrained conversational speech domains—speeches and broadcast news—and three languages: European English, European Spanish, and Mandarin Chinese. Accurate translation of unrestricted speech is well beyond the capability of today’s state-of-the-art research systems. Therefore, advances are needed to improve the state-of the-art technologies for speech recognition and speech translation.
Our pick of the week by @lina97337786: "Towards a Deep Understanding of Multilingual End-to-End Speech Translation" by @hrsun42, @YikunLei et al., Findings EMNLP 2023.
#NLP #NLProc #EMNLP2023 #speech #translation #multilingual
🎉 AI-GAP workshop wrapped up yesterday!
Huge thanks to the participants, speakers, and organizers -- we were very happy, as you can see :)
@donatellado @BeatriceSavoldi @costanzalfieri @ECappu Clara Punzi & Laura State
🎊 I'm very happy to share that the paper "Direct Speech Translation for Automatic Subtitling" accepted by the #TACL journal is now published and available at:
#NLP #NLProc #subtitling #speech #translation
📢 @BeatriceSavoldi from @fbk_mt (@FBK_research) is co-organizing the AI GAP Workshop, held in collaboration with @Unipisa, @univaq, and @scuolanormale!
👉 Check the website for more information:
#AI #ArtificialIntelligence #AlgorithmicBias