58 citations · 94 across the 38 of their papers we have counts for
10 papers · 1 filter
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…
Speech LLMs in Low-Resource Scenarios: Data Volume Requirements and the Impact of Pretraining on High-Resource Languages
Seraphina Fong, Marco Matassoni, Alessio Brutti
Large language models (LLMs) have demonstrated potential in handling spoken inputs for high-resource languages, reaching state-of-the-art performance in various tasks. However, the…
Input Conditioned Layer Dropping in Speech Foundation Models
Abdul Hannan, Daniele Falavigna, Alessio Brutti
Curating foundation speech models for edge and IoT settings, where computational resources vary over time, requires dynamic architectures featuring adaptable reduction strategies.…
The Eloquence team submission for task 1 of MLC-SLM challenge
Lorenzo Concina, Jordi Luque, Alessio Brutti +2
In this paper, we present our studies and experiments carried out for the task 1 of the Challenge and Workshop on Multilingual Conversational Speech Language Model (MLC-SLM), which…
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…