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 @FBKZhihangXie: "PHRASED: Phrase Dictionary Biasing for Speech Translation" by Peidong Wang, Jian Xue, Rui Zhao, @ChenJunkun, Aswin Shanmugam Subramanian, and Jinyu Li (2025).
#Speech #SpeechAI #Translation #ST #SpeechTranslation
🚀 Boost rare-phrase translation in speech! Uses **bilingual dictionaries** to dynamically bias outputs.
✅ **+21%** recall in streaming ST
✅ **+85%** in multimodal LLMs
🔗: http://arxiv.org/abs/2506.09175
FAMA è il primo foundation model vocale open-science per ita e eng, sviluppato da FBK. Riconosce e traduce la voce usando solo dati e strumenti pubblici: oltre 150.000 ore di audio open, codice e processi completamente accessibili.
@fbk_stek @fbk_mt
https://magazine.fbk.eu/it/news/la-prima-famiglia-di-modelli-open-science-per-il-riconoscimento-vocale-e-la-traduzione-del-parlato/
Emanuele Pianta Award for the Best Master’s Thesis in Computational Linguistics submitted at an Italian university and defended between August 1st 2024 and July 31st 2025
- Deadline: August 1st, 2025 (11:59 pm CEST)
- All details online: https://clic2025.unica.it/emanuele-pianta-award-for-the-best-masters-thesis/
Our pick of the week by @DennisFucci: "Speech Representation Analysis Based on Inter- and Intra-Model Similarities" by Yassine El Kheir, Ahmed Ali, and Shammur Absar Chowdhury (ICASSP Workshops 2024)
#speech #speechtech
Findings from https://ieeexplore.ieee.org/document/10669908 show that speech SSL models converge on similar embedding spaces, but via different routes. While overall representations align, individual neurons learn distinct localized concepts.
Interesting read! @fbk_mt