4 citations · 5 across the 6 of their papers we have counts for
6 papers
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…
LiSTEN: Learning Soft Token Embeddings for Neural Audio LLMs
Pooneh Mousavi, Shubham Gupta, Cem Subakan +1
Foundation models based on large language models (LLMs) have shown great success in handling various tasks and modalities. However, adapting these models for general-purpose audio-…
Investigating the Effectiveness of Explainability Methods in Parkinson's Detection from Speech
Eleonora Mancini, Francesco Paissan, Paolo Torroni +2
Speech impairments in Parkinson's disease (PD) provide significant early indicators for diagnosis. While models for speech-based PD detection have shown strong performance, their i…
TorchAudio 2.1: Advancing speech recognition, self-supervised learning, and audio processing components for PyTorch
Jeff Hwang, Moto Hira, Caroline Chen +21
TorchAudio is an open-source audio and speech processing library built for PyTorch. It aims to accelerate the research and development of audio and speech technologies by providing…
Simulated Annealing in Early Layers Leads to Better Generalization
Amirmohammad Sarfi, Zahra Karimpour, Muawiz Chaudhary +4
Recently, a number of iterative learning methods have been introduced to improve generalization. These typically rely on training for longer periods of time in exchange for improve…
Posthoc Interpretation via Quantization
Francesco Paissan, Cem Subakan, Mirco Ravanelli
In this paper, we introduce a new approach, called Posthoc Interpretation via Quantization (PIQ), for interpreting decisions made by trained classifiers. Our method utilizes vector…