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MambAdapter: Lightweight Mamba-Based Adapters for Parameter-Efficient Transfer Learning in Speech and Audio
Salman Hussain Ali, Umberto Cappellazzo, Mirco Ravanelli
Fine-tuning Transformer-based foundation models has become the dominant strategy for domain adaptation in audio and speech processing. To reduce the computational and memory costs…
Comparison of Speech Tasks in Human Expert and Machine Detection of Parkinson's Disease
Peter Plantinga, Roozbeh Sattari, Karine Marcotte +8
The speech of people with Parkinson's Disease (PD) has been shown to hold important clues about the presence and progression of the disease. We investigate the factors based on whi…
From Black Box to Biomarker: Sparse Autoencoders for Interpreting Speech Models of Parkinson's Disease
Peter Plantinga, Jen-Kai Chen, Roozbeh Sattari +2
Speech holds promise as a cost-effective and non-invasive biomarker for neurological conditions such as Parkinson's disease (PD). While deep learning systems trained on raw audio c…
Phoneme Discretized Saliency Maps for Explainable Detection of AI-Generated Voice
Shubham Gupta, Mirco Ravanelli, Pascal Germain +1
In this paper, we propose Phoneme Discretized Saliency Maps (PDSM), a discretization algorithm for saliency maps that takes advantage of phoneme boundaries for explainable detectio…
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…