2 citations · 3 across the 6 of their papers we have counts for
4 papers · 1 filter
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…
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…
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…