NESPOLE! System has been developed using two scenarios: the tourism scenario and the first aid medical assistance scenario. During the project life three main data collection have been carried on in order to develop the first and the second showcase. During the first year 191 dialogues have been collected. There are 62 German dialogues recorded, 61 Italian, 37 English and 31 French. Particularly an amount of 6 hours of dialogues for Italian and French, 7 hours for English, 8 hours for German has been recorded. Dialogues were about five predefined tourism scenarios. During the last year two major data collections have been carried on: the first one aimed at expanding the tourism scenario and the second one at addressing the medical domain. For the monolingual data collection five tourism scenarios were developed; 66 dialogues were recorded yielding 994.57 minutes of data: 243.52 minutes comprised in sixteen English dialogues, 246 minutes in sixteen German dialogues, 272.52 minutes in seventeen French dialogues and 232.53 minutes in seventeen Italian dialogues. The data collection on the medical domain involved Italian, English and German languages. A total of 49 dialogues were collected. The recording results in a total of 8 hours 25 minutes of audio files.
Our #PickOfTheWeek 📚: “Training Dynamics Impact Post-Training Quantization Robustness” by @actatjer, @n_ajroldi, @jonasgeiping
Pick of the week @fbk_mt
Cool paper on how robustness to quantization in LLMs is also impacted by training conditions, instead of just the number of training tokens!
https://arxiv.org/abs/2510.06213
Our #PickOfTheWeek 📚: “High-Fidelity Simultaneous Speech-To-Speech Translation” by @t0m1ab, @lmazare, @EXGRV, @ptrkprz, @honualx and @neilzegh
🎙️➡️🌍 This work shows how a full-duplex SpeechLLM architecture can be adapted for simultaneous S2ST and S2TT.
Can LLM Agents Learn how to behave According to your preferences?
PersonaAgent is a framework that combines personalized memory and tool use to help LLM agents adapt their actions to individual users without fine-tuning the underlying model.
Our #PickOfTheWeek, selected by @mgaido91: "Alignment Is All You Need: Instruction-Free Training for General Audio-Language Models" by Xuanru Zhou, Yiwen Shao, Jiahong Li, and Dong Yu 🎧
Can instruction-free training power the next generation of audio-language models?
Cool to see we are not the only ones working on building SpeechLLMs with instruction-following capabilities without requiring LLM adaptation and instruction-following data that is expensive to collect!
https://arxiv.org/abs/2608.18132
@fbk_mt