2 citations · 6 across the 16 of their papers we have counts for
6 papers · 1 filter
Parameter-Efficient Transfer Learning of Audio Spectrogram Transformers
Umberto Cappellazzo, Daniele Falavigna, Alessio Brutti +1
Parameter-efficient transfer learning (PETL) methods have emerged as a solid alternative to the standard full fine-tuning approach. They only train a few extra parameters for each…
Continual Contrastive Spoken Language Understanding
Umberto Cappellazzo, Enrico Fini, Muqiao Yang +3
Recently, neural networks have shown impressive progress across diverse fields, with speech processing being no exception. However, recent breakthroughs in this area require extens…
Training dynamic models using early exits for automatic speech recognition on resource-constrained devices
George August Wright, Umberto Cappellazzo, Salah Zaiem +5
The ability to dynamically adjust the computational load of neural models during inference is crucial for on-device processing scenarios characterised by limited and time-varying c…
Sequence-Level Knowledge Distillation for Class-Incremental End-to-End Spoken Language Understanding
Umberto Cappellazzo, Muqiao Yang, Daniele Falavigna +1
The ability to learn new concepts sequentially is a major weakness for modern neural networks, which hinders their use in non-stationary environments. Their propensity to fit the c…
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…
Scaling strategies for on-device low-complexity source separation with Conv-Tasnet
Mohamed Nabih Ali, Francesco Paissan, Daniele Falavigna +1
Recently, several very effective neural approaches for single-channel speech separation have been presented in the literature. However, due to the size and complexity of these mode…