15 papers
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…
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…
Multimodal Speaker Verification as a Threat to Speaker Anonymization
Ashi Garg, Cristina Aggazzotti, Leibny Paola García-Perera +1
Most automatic speaker verification (ASV) systems operate on individual utterances, despite real-world interactions typically consisting of multiple utterances. As speech accumulat…
ProSDD: Learning Prosodic Representations for Speech Deepfake Detection against Expressive and Emotional Attacks
Aurosweta Mahapatra, Ismail Rasim Ulgen, Kong Aik Lee +2
Speech deepfake detection (SDD) systems perform well on standard benchmarks datasets but often fail to generalize to expressive and emotional spoofing attacks. Many methods rely on…
DiffAnon: Diffusion-based Prosody Control for Voice Anonymization
Ismail Rasim Ulgen, Zexin Cai, Nicholas Andrews +2
To preserve or not to preserve prosody is a central question in voice anonymization. Prosody conveys meaning and affect, yet is tightly coupled with speaker identity. Existing meth…
Integrated Spoofing-Robust Automatic Speaker Verification via a Three-Class Formulation and LLR
Kai Tan, Lin Zhang, Ruiteng Zhang +6
Spoofing-robust automatic speaker verification (SASV) aims to integrate automatic speaker verification (ASV) and countermeasure (CM). A popular solution is fusion of independent AS…
Can LLMs Help Localize Fake Words in Partially Fake Speech?
Lin Zhang, Thomas Thebaud, Zexin Cai +5
Large language models (LLMs), trained on large-scale text, have recently attracted significant attention for their strong performance across many tasks. Motivated by this, we inves…
Universal Speech Content Factorization
Henry Li Xinyuan, Zexin Cai, Lin Zhang +5
We propose Universal Speech Content Factorization (USCF), a simple and invertible linear method for extracting a low-rank speech representation in which speaker timbre is suppresse…
Rapidly Adapting to New Voice Spoofing: Few-Shot Detection of Synthesized Speech Under Distribution Shifts
Ashi Garg, Zexin Cai, Henry Li Xinyuan +5
We address the challenge of detecting synthesized speech under distribution shifts -- arising from unseen synthesis methods, speakers, languages, or audio conditions -- relative to…
Scalable Controllable Accented TTS
Henry Li Xinyuan, Zexin Cai, Ashi Garg +5
We tackle the challenge of scaling accented TTS systems, expanding their capabilities to include much larger amounts of training data and a wider variety of accent labels, even for…
Uncertainty Distillation: Teaching Language Models to Express Semantic Confidence
Sophia Hager, David Mueller, Kevin Duh +1
As large language models (LLMs) are increasingly used for factual question-answering, it becomes more important for LLMs to have the capability to communicate the likelihood that t…
ShiftySpeech: A Large-Scale Synthetic Speech Dataset with Distribution Shifts
Ashi Garg, Zexin Cai, Lin Zhang +6
The problem of synthetic speech detection has enjoyed considerable attention, with recent methods achieving low error rates across several established benchmarks. However, to what…
GenVC: Self-Supervised Zero-Shot Voice Conversion
Zexin Cai, Henry Li Xinyuan, Ashi Garg +5
Most current zero-shot voice conversion methods rely on externally supervised components, particularly speaker encoders, for training. To explore alternatives that eliminate this d…
HLTCOE JHU Submission to the Voice Privacy Challenge 2024
Henry Li Xinyuan, Zexin Cai, Ashi Garg +5
We present a number of systems for the Voice Privacy Challenge, including voice conversion based systems such as the kNN-VC method and the WavLM voice Conversion method, and text-t…
Privacy versus Emotion Preservation Trade-offs in Emotion-Preserving Speaker Anonymization
Zexin Cai, Henry Li Xinyuan, Ashi Garg +5
Advances in speech technology now allow unprecedented access to personally identifiable information through speech. To protect such information, the differential privacy field has…