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Alessandro Vinciarelli

6 accepted papers

2024

Exploring 3D Human Pose Estimation and Forecasting from the Robot’s Perspective: The HARPER Dataset

Andrea Avogaro, Andrea Toaiari, Federico Cunico, Xiangmin Xu, Haralambos Dafas, Alessandro Vinciarelli +2
IROS 2024poster

We introduce HARPER, a novel dataset for 3D body pose estimation and forecasting in dyadic interactions between users and Spot, the quadruped robot manufactured by Boston Dynamics. The key-novelty of HARPER is its focus on the robot’s perspective, i.e., on the data captured by the robot’s sensors. T…

Cited by 3Source ↗Scholar ↗
2023

Multi-Local Attention for Speech-Based Depression Detection

Fuxiang Tao, Xuri Ge, Wei Ma, Anna Esposito, Alessandro Vinciarelli
ICASSP 2023accepted

This article shows that an attention mechanism, the Multi-Local Attention, can improve a depression detection approach based on Long Short-Term Memory Networks. Besides leading to higher performance metrics (e.g., Accuracy and F1 Score), Multi-Local Attention improves two other aspects of the approa…

Cited by 0Source ↗Scholar ↗
2022

Attachment Recognition in School-Age Children: A Multimodal Approach Based on Language and Paralanguage Analysis

Huda Alsofyani, Alessandro Vinciarelli
ICASSP 2022accepted

Attachment is the psychological construct accounting for whether parents address effectively physical and emotional needs of their children or not. The approach proposed in this work recognizes whether a child is secure or insecure, the two major attachment conditions an individual can belong to. Th…

Cited by 0Source ↗Scholar ↗
2022

Thin Slices of Depression: Improving Depression Detection Performance Through Data Segmentation

Rawan Alsarrani, Anna Esposito, Alessandro Vinciarelli
ICASSP 2022accepted

The computing community is making major efforts towards automatic detection of depression, a serious pathology that affects roughly 4.4% of the world’s population. One of the main difficulties is the collection of data aimed at training models capable to learn differences between depressed and non-d…

Cited by 0Source ↗Scholar ↗
2018

Depression Speaks: Automatic Discrimination between Depressed and Non-Depressed Speakers Based on Nonverbal Speech Features

Filomena Scibelli, Giorgio Roffo, Mohammad Tayarani, Luca Bartoli, Gaetano De Mattia, Anna Esposito +1
ICASSP 2018accepted

This article proposes an automatic approach - based on nonverbal speech features - aimed at the automatic discrimination between depressed and non-depressed speakers. The experiments have been performed over one of the largest corpora collected for such a task in the literature (62 patients diagnose…

Cited by 0Source ↗Scholar ↗
2017

Infinite Latent Feature Selection: A Probabilistic Latent Graph-Based Ranking Approach

Giorgio Roffo, Simone Melzi, Umberto Castellani, Alessandro Vinciarelli
ICCV 2017poster

Feature selection is playing an increasingly significant role with respect to many computer vision applications spanning from object recognition to visual object tracking. However, most of the recent solutions in feature selection are not robust across different and heterogeneous set of data. In thi…

Cited by 317PDF ↗Scholar ↗