6 papers · 1 filter
Linguistically Augmented Audio Speech Data (LinguAS)
Ashley R. Keaton, Zahra Khanjani, Christine Mallinson +1
Maliciously-created fake speech, including deepfaked and spoofed audio, is proliferating at an alarming rate, and detection models are racing to stay ahead of the curve. Yet, most…
Multi-Speaker Conversational Audio Deepfake: Taxonomy, Dataset and Pilot Study
Alabi Ahmed, Vandana Janeja, Sanjay Purushotham
The rapid advances in text-to-speech (TTS) technologies have made audio deepfakes increasingly realistic and accessible, raising significant security and trust concerns. While exis…
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…