58 citations · 94 across the 38 of their papers we have counts for
12 papers · 1 filter
The Eloquence submission for Task 2 of the Interspeech 2026 MLC-SLM challenge
Jordi Luque, Lorenzo Concina, Marco Matassoni +2
This paper details the Eloquence team's approach to Task 2 of the 2nd MLC-SLM challenge at Interspeech 2026, which involves multilingual Multiple-Choice Question Answering (MCQA) a…
SpeechLLM Meets Federated Learning for End-to-End ASR: English and Italian Case Studies
Mohamed Nabih Ali, Daniele Falavigna, Alessio Brutti
Federated learning (FL) enables privacy-preserving training of automatic speech recognition (ASR) systems across distributed data sources, yet its application to large-scale speech…
MEUSLI: a Multilingual Projector for LLM-based ASR and Beyond
Lorenzo Concina, Seraphina Fong, Marco Matassoni +1
Lightweight projectors are an established way to connect pre-trained speech encoders with large language models (LLMs), mapping acoustic features into token-level embeddings for ta…
LuxSQA: Ask Me in Luxembourgish with TTS-Augmented Spoken Question Answering
Nina Hosseini-Kivanani, Marco Matassoni, Alessio Brutti
Spoken Question Answering (SQA) remains largely focused on high-resource languages and carefully recorded speech, limiting the reach of speech-LLM methods in low-resource settings.…
MLMA: Towards Multilingual ASR With Mamba-based Architectures
Mohamed Nabih Ali, Daniele Falavigna, Alessio Brutti
Multilingual automatic speech recognition (ASR) remains a challenging task, especially when balancing performance across high- and low-resource languages. Recent advances in sequen…
Splitformer: An improved early-exit architecture for automatic speech recognition on edge devices
Maxence Lasbordes, Daniele Falavigna, Alessio Brutti
The ability to dynamically adjust the computational load of neural models during inference in a resource aware manner is crucial for on-device processing scenarios, characterised b…
FAMA: The First Large-Scale Open-Science Speech Foundation Model for English and Italian
Sara Papi, Marco Gaido, Luisa Bentivogli +6
The development of speech foundation models (SFMs) like Whisper and SeamlessM4T has significantly advanced the field of speech processing. However, their closed nature--with inacce…
The Warmup Dilemma: How Learning Rate Strategies Impact Speech-to-Text Model Convergence
Marco Gaido, Sara Papi, Luisa Bentivogli +6
Training large-scale models presents challenges not only in terms of resource requirements but also in terms of their convergence. For this reason, the learning rate (LR) is often…
Granary: Speech Recognition and Translation Dataset in 25 European Languages
Nithin Rao Koluguri, Monica Sekoyan, George Zelenfroynd +12
Multi-task and multilingual approaches benefit large models, yet speech processing for low-resource languages remains underexplored due to data scarcity. To address this, we presen…
MOSEL: 950,000 Hours of Speech Data for Open-Source Speech Foundation Model Training on EU Languages
Marco Gaido, Sara Papi, Luisa Bentivogli +6
The rise of foundation models (FMs), coupled with regulatory efforts addressing their risks and impacts, has sparked significant interest in open-source models. However, existing s…
Federating Dynamic Models using Early-Exit Architectures for Automatic Speech Recognition on Heterogeneous Clients
Mohamed Nabih Ali, Alessio Brutti, Daniele Falavigna
Automatic speech recognition models require large amounts of speech recordings for training. However, the collection of such data often is cumbersome and leads to privacy concerns.…
Improving the Intent Classification accuracy in Noisy Environment
Mohamed Nabih Ali, Alessio Brutti, Daniele Falavigna
Intent classification is a fundamental task in the spoken language understanding field that has recently gained the attention of the scientific community, mainly because of the fea…