24 citations · 60 across the 37 of their papers we have counts for
19 papers · 1 filter
Exploiting Acoustic and Content-Oriented Speaker Verification Attacks Against Multilingual Voice Anonymization
Ridwan Arefeen, Ze Li, Rong Tong +2
Attacker ASV systems for voice anonymization have been studied primarily in English, leaving their behavior in multilingual settings largely unexplored. Conventional ASV has shown…
Child-Centric Voice Anonymization in Single and Multi-Speaker Speech via Domain-Adapted SSL Models
Pranav Tushar, Xiao Xiao Miao, Rong Tong
Voice anonymization aims to protect speaker identity while preserving linguistic content and speech usability. However, most anonymization systems are developed on adult speech, le…
DAST: A Dual-Stream Voice Anonymization Attacker with Staged Training
Ridwan Arefeen, Xiaoxiao Miao, Rong Tong +3
Voice anonymization masks vocal traits while preserving linguistic content, which may still leak speaker-specific patterns. To assess and strengthen privacy evaluation, we propose…
Toward Multimodal Industrial Fault Analysis: A Single-Speed Chain Conveyor Dataset with Audio and Vibration Signals
Zhang Chen, Yucong Zhang, Xiaoxiao Miao +1
We introduce a multimodal industrial fault analysis dataset collected from a single-speed chain conveyor (SSCC) system, targeting system-level fault detection in production lines.…
Training Dynamics-Aware Multi-Factor Curriculum Learning for Target Speaker Extraction
Yun Liu, Xuechen Liu, Xiaoxiao Miao +1
Target speaker extraction (TSE) aims to isolate a specific speaker's voice from multi-speaker mixtures. Despite strong benchmark results, real-world performance often degrades due…
SegReConcat: A Data Augmentation Method for Voice Anonymization Attack
Ridwan Arefeen, Xiaoxiao Miao, Rong Tong +2
Anonymization of voice seeks to conceal the identity of the speaker while maintaining the utility of speech data. However, residual speaker cues often persist, which pose privacy r…
SEF-MK: Speaker-Embedding-Free Voice Anonymization through Multi-k-means Quantization
Beilong Tang, Xiaoxiao Miao, Xin Wang +1
Voice anonymization protects speaker privacy by concealing identity while preserving linguistic and paralinguistic content. Self-supervised learning (SSL) representations encode li…
SecureSpeech: Prompt-based Speaker and Content Protection
Belinda Soh Hui Hui, Xiaoxiao Miao, Xin Wang
Given the increasing privacy concerns from identity theft and the re-identification of speakers through content in the speech field, this paper proposes a prompt-based speech gener…
Automated evaluation of children's speech fluency for low-resource languages
Bowen Zhang, Nur Afiqah Abdul Latiff, Justin Kan +4
Assessment of children's speaking fluency in education is well researched for majority languages, but remains highly challenging for low resource languages. This paper proposes a s…
Libri2Vox Dataset: Target Speaker Extraction with Diverse Speaker Conditions and Synthetic Data
Yun Liu, Xuechen Liu, Xiaoxiao Miao +1
Target speaker extraction (TSE) is essential in speech processing applications, particularly in scenarios with complex acoustic environments. Current TSE systems face challenges in…
Adapting General Disentanglement-Based Speaker Anonymization for Enhanced Emotion Preservation
Xiaoxiao Miao, Yuxiang Zhang, Xin Wang +3
A general disentanglement-based speaker anonymization system typically separates speech into content, speaker, and prosody features using individual encoders. This paper explores h…
A Benchmark for Multi-speaker Anonymization
Xiaoxiao Miao, Ruijie Tao, Chang Zeng +1
Privacy-preserving voice protection approaches primarily suppress privacy-related information derived from paralinguistic attributes while preserving the linguistic content. Existi…
Speaker-Text Retrieval via Contrastive Learning
Xuechen Liu, Xin Wang, Erica Cooper +2
In this study, we introduce a novel cross-modal retrieval task involving speaker descriptions and their corresponding audio samples. Utilizing pre-trained speaker and text encoders…
SynVox2: Towards a privacy-friendly VoxCeleb2 dataset
Xiaoxiao Miao, Xin Wang, Erica Cooper +5
The success of deep learning in speaker recognition relies heavily on the use of large datasets. However, the data-hungry nature of deep learning methods has already being question…
VoicePAT: An Efficient Open-source Evaluation Toolkit for Voice Privacy Research
Sarina Meyer, Xiaoxiao Miao, Ngoc Thang Vu
Speaker anonymization is the task of modifying a speech recording such that the original speaker cannot be identified anymore. Since the first Voice Privacy Challenge in 2020, alon…
Speaker anonymization using orthogonal Householder neural network
Xiaoxiao Miao, Xin Wang, Erica Cooper +2
Speaker anonymization aims to conceal a speaker's identity while preserving content information in speech. Current mainstream neural-network speaker anonymization systems disentang…
Analyzing Language-Independent Speaker Anonymization Framework under Unseen Conditions
Xiaoxiao Miao, Xin Wang, Erica Cooper +2
In our previous work, we proposed a language-independent speaker anonymization system based on self-supervised learning models. Although the system can anonymize speech data of any…
Language-Independent Speaker Anonymization Approach using Self-Supervised Pre-Trained Models
Xiaoxiao Miao, Xin Wang, Erica Cooper +2
Speaker anonymization aims to protect the privacy of speakers while preserving spoken linguistic information from speech. Current mainstream neural network speaker anonymization sy…
Adaptive Margin Circle Loss for Speaker Verification
Runqiu Xiao
Deep-Neural-Network (DNN) based speaker verification sys-tems use the angular softmax loss with margin penalties toenhance the intra-class compactness of speaker embeddings,which a…