The PF-STAR project intended to contribute to establish future activities in the field of multisensorial and multilingual communication (interface technologies) on firmer bases by providing technological baselines, comparative evaluations, and assessment of prospects of core technologies, which future research and development efforts can build from. To this end, the project addressed three crucial areas: technologies for speech-to-speech translation, the detection and expressions of emotional states, and core speech technologies for children. For each of them, promising technologies/approaches were selected, further developed and aligned towards common baselines. The results were assessed and evaluated with respect to both their performances and future prospects. To maximise the impact, the duration of the project was limited to 24 months, and the workplan was designed to delivered results in two stages: at mid-project term (month 14), and at the end of the project. This permitted to make relevant results available as soon as possible, and in particular on time for them to be used during the preparatory phase of the first call of FP6. The Lehrstuhl für Informatik 6 was involved in the comparative evaluation and further development of speech translation technologies. The statistical approach was compared to an interlingua based approach. After the evaluation phase, the two approaches were further developed and aligned towards common baselines. PF-STAR was supported by the European Union.
Cosa chiedono davvero gli italiani all’intelligenza artificiale?
FBK in collaborazione con RiTA lancia un’indagine aperta a tutte/i per capire usi reali, abitudini e bisogni.
Bastano 10 minuti per partecipare, scopri di più: https://magazine.fbk.eu/it/news/italiani-e-ia-cosa-chiediamo-veramente-allintelligenza-artificiale/
🚀 Last call for the Model Compression for Machine Translation task at #WMT2025 (co-located with #EMNLP2025)!
Test data out on June 19 ➡️ 2 weeks for evaluation!
Can you shrink an LLM and keep translation quality high?
👉 https://www2.statmt.org/wmt25/model-compression.html #NLP #ML #LLM #ModelCompression
Our pick of the week by @beomseok_lee_: "ALAS: Measuring Latent Speech-Text Alignment For Spoken Language Understanding In Multimodal LLMs" by Pooneh Mousavi, @yingzhi_wang, @mirco_ravanelli, and @CemSubakan (2025)
#SLU #speech #multimodal #LLM
Speech-language models show promise in multimodal tasks—but how well are speech & text actually aligned? 🤔
This paper https://arxiv.org/abs/2505.19937 proposes a new metric to measure layer-wise correlation between the two, with a focus on SLU tasks. 🔍🗣️📄
🔍 Ciao! Stiamo studiando come l'AI viene usata in Italia e per farlo abbiamo costruito un sondaggio!
👉https://bocconi.eu.qualtrics.com/jfe/form/SV_2nTelXaXvJlinbg (è anonimo, dura ~10 m, se partecipi o lo diffondi ci aiuti un sacco🙏)
Ci interessa anche raggiungere persone che non si occupano di AI!