3 papers
cs.CL2026
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…
cs.CL2025
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…
cs.CL2024
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.…