29 citations · 86 across the 50 of their papers we have counts for
36 papers · 1 filter
Not All Attacks Are Learned Equally in Speech Deepfake Detection
Avantika Singh, Aurosweta Mahapatra, Ismail Rasim Ulgen +3
Speech deepfake detection (SDD) models are trained on multi-attack datasets containing diverse spoofing systems, such as text-to-speech (TTS) and voice conversion (VC). In standard…
Brain2Speech-Net: Fast and Intelligible Brain-to-Speech Synthesis Without Text Decoding
Shreeram Suresh Chandra, Zexin Cai, Yu Tsao +2
The loss of speech limits communication for individuals with paralysis. Direct neural-to-speech synthesis is challenging due to the limited availability of neural data for training…
AffectDF: The Most Comprehensive Benchmark for Speech Deepfake Detection against Emotionally Expressive Attacks
Aurosweta Mahapatra, Xiutian Zhao, Shreeram Suresh Chandra +7
Speech deepfake detection (SDD) systems achieve strong performance on conventional benchmarks; however, existing datasets provide limited coverage of emotionally expressive and rec…
Disentangling the Interpretive and Predictive Roles of LIWC: Controlled Substitution in Depression-Related Classification
Hsiang-Chen Yeh, Xiutian Zhao, Aurosweta Mahapatra +3
Linguistic Inquiry and Word Count (LIWC) provides auditable psycholinguistic categories that are widely used to interpret depression-related language, but its incremental predictiv…
TRACE-EVC: Text-Guided Relative Affective Control for Zero-Shot Emotional Voice Conversion
Zihan Zhang, Shreeram Suresh Chandra, Zongyang Du +5
Traditional emotional voice conversion (EVC) conditions generation on explicit target emotions like labels or references, defining the target affective state but omitting the direc…
Who is Speaking or Who is Depressed? A Controlled Study of Speaker Leakage in Speech-Based Depression Detection
Hsiang-Chen Yeh, Luqi Sun, Aurosweta Mahapatra +3
This study investigates whether speech-based depression detection models learn depression-related acoustic biomarkers or instead rely on speaker identity cues. Using the DAIC-WOZ d…