11 citations · 11 across the 20 of their papers we have counts for
6 papers · 1 filter
Listening for Expert Identified Linguistic Features: Assessment of Audio Deepfake Discernment among Undergraduate Students
Noshaba N. Bhalli, Nehal Naqvi, Chloe Evered +2
This paper evaluates the impact of training undergraduate students to improve their audio deepfake discernment ability by listening for expert-defined linguistic features. Such fea…
Toward Transdisciplinary Approaches to Audio Deepfake Discernment
Vandana P. Janeja, Christine Mallinson
This perspective calls for scholars across disciplines to address the challenge of audio deepfake detection and discernment through an interdisciplinary lens across Artificial Inte…
ALDAS: Audio-Linguistic Data Augmentation for Spoofed Audio Detection
Zahra Khanjani, Christine Mallinson, James Foulds +1
Spoofed audio, i.e. audio that is manipulated or AI-generated deepfake audio, is difficult to detect when only using acoustic features. Some recent innovative work involving AI-spo…
Investigating Causal Cues: Strengthening Spoofed Audio Detection with Human-Discernible Linguistic Features
Zahra Khanjani, Tolulope Ale, Jianwu Wang +3
Several types of spoofed audio, such as mimicry, replay attacks, and deepfakes, have created societal challenges to information integrity. Recently, researchers have worked with so…
Harnessing Feature Clustering For Enhanced Anomaly Detection With Variational Autoencoder And Dynamic Threshold
Tolulope Ale, Nicole-Jeanne Schlegel, Vandana P. Janeja
We introduce an anomaly detection method for multivariate time series data with the aim of identifying critical periods and features influencing extreme climate events like snowmel…
Assessing Annotation Accuracy in Ice Sheets Using Quantitative Metrics
Bayu Adhi Tama, Vandana Janeja, Sanjay Purushotham
The increasing threat of sea level rise due to climate change necessitates a deeper understanding of ice sheet structures. This study addresses the need for accurate ice sheet data…