11 citations · 24 across the 21 of their papers we have counts for
8 papers · 1 filter
What You Train Is What You Get: Gender Bias, Training Composition, and Post-Hoc Mitigation in Audio Deepfake Detection
Aishwarya R. Fursule, Vamshi Nallaguntla, Shruti Kshirsagar +1
Audio deepfake detection models determine whether speech is genuine or artificially generated, but high overall accuracy can mask substantial performance disparities across demogra…
Towards Trustworthy Audio Deepfake Detection: A Systematic Framework for Diagnosing and Mitigating Gender Bias
Aishwarya Fursule, Shruti Kshirsagar, Anderson R. Avila
Audio deepfake detection systems are increasingly deployed in high-stakes security applications, yet their fairness across demographic groups remains critically underexamined. Prio…
Phoneme-Level Deepfake Detection Across Emotional Conditions Using Self-Supervised Embeddings
Vamshi Nallaguntla, Shruti Kshirsagar, Anderson R. Avila
Recent advances in emotional voice conversion (EVC) have enabled the generation of expressive synthetic speech, raising new concerns in audio deepfake detection. Existing approache…
PhonemeDF: A Synthetic Speech Dataset for Audio Deepfake Detection and Naturalness Evaluation
Vamshi Nallaguntla, Aishwarya Fursule, Shruti Kshirsagar +1
The growing sophistication of speech generated by Artificial Intelligence (AI) has introduced new challenges in audio deepfake detection. Text-to-speech (TTS) and voice conversion…
Investigating the Impact of Speech Enhancement on Audio Deepfake Detection in Noisy Environments
Anacin, Angela, Shruti Kshirsagar +1
Logical Access (LA) attacks, also known as audio deepfake attacks, use Text-to-Speech (TTS) or Voice Conversion (VC) methods to generate spoofed speech data. This can represent a s…
Gender Fairness in Audio Deepfake Detection: Performance and Disparity Analysis
Aishwarya Fursule, Shruti Kshirsagar, Anderson R. Avila
Audio deepfake detection aims to detect real human voices from those generated by Artificial Intelligence (AI) and has emerged as a significant problem in the field of voice biomet…
Improving the Robustness of DistilHuBERT to Unseen Noisy Conditions via Data Augmentation, Curriculum Learning, and Multi-Task Enhancement
Heitor R. Guimarães, Arthur Pimentel, Anderson R. Avila +2
Self-supervised speech representation learning aims to extract meaningful factors from the speech signal that can later be used across different downstream tasks, such as speech an…
Low-bit Shift Network for End-to-End Spoken Language Understanding
Anderson R. Avila, Khalil Bibi, Rui Heng Yang +3
Deep neural networks (DNN) have achieved impressive success in multiple domains. Over the years, the accuracy of these models has increased with the proliferation of deeper and mor…