1 citations · 1 across the 11 of their papers we have counts for
12 papers
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…
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…
Content Anonymization for Privacy in Long-form Audio
Cristina Aggazzotti, Ashi Garg, Zexin Cai +1
Voice anonymization techniques have been found to successfully obscure a speaker's acoustic identity in short, isolated utterances in benchmarks such as the VoicePrivacy Challenge.…
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…
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…