18 citations · 71 across the 14 of their papers we have counts for
14 papers
Enhancing the vocal range of single-speaker singing voice synthesis with melody-unsupervised pre-training
Shaohuan Zhou, Xu Li, Zhiyong Wu +2
The single-speaker singing voice synthesis (SVS) usually underperforms at pitch values that are out of the singer's vocal range or associated with limited training samples. Based o…
Quantitative Evidence on Overlooked Aspects of Enrollment Speaker Embeddings for Target Speaker Separation
Xiaoyu Liu, Xu Li, Joan Serrà
Single channel target speaker separation (TSS) aims at extracting a speaker's voice from a mixture of multiple talkers given an enrollment utterance of that speaker. A typical deep…
A Hierarchical Speaker Representation Framework for One-shot Singing Voice Conversion
Xu Li, Shansong Liu, Ying Shan
Typically, singing voice conversion (SVC) depends on an embedding vector, extracted from either a speaker lookup table (LUT) or a speaker recognition network (SRN), to model speake…
Spoofing-Aware Speaker Verification by Multi-Level Fusion
Haibin Wu, Lingwei Meng, Jiawen Kang +5
Recently, many novel techniques have been introduced to deal with spoofing attacks, and achieve promising countermeasure (CM) performances. However, these works only take the stand…
Characterizing the adversarial vulnerability of speech self-supervised learning
Haibin Wu, Bo Zheng, Xu Li +3
A leaderboard named Speech processing Universal PERformance Benchmark (SUPERB), which aims at benchmarking the performance of a shared self-supervised learning (SSL) speech model a…
Channel-wise Gated Res2Net: Towards Robust Detection of Synthetic Speech Attacks
Xu Li, Xixin Wu, Hui Lu +2
Existing approaches for anti-spoofing in automatic speaker verification (ASV) still lack generalizability to unseen attacks. The Res2Net approach designs a residual-like connection…
VAENAR-TTS: Variational Auto-Encoder based Non-AutoRegressive Text-to-Speech Synthesis
Hui Lu, Zhiyong Wu, Xixin Wu +4
This paper describes a variational auto-encoder based non-autoregressive text-to-speech (VAENAR-TTS) model. The autoregressive TTS (AR-TTS) models based on the sequence-to-sequence…
Improving the Adversarial Robustness for Speaker Verification by Self-Supervised Learning
Haibin Wu, Xu Li, Andy T. Liu +3
Previous works have shown that automatic speaker verification (ASV) is seriously vulnerable to malicious spoofing attacks, such as replay, synthetic speech, and recently emerged ad…
Adversarial defense for automatic speaker verification by cascaded self-supervised learning models
Haibin Wu, Xu Li, Andy T. Liu +3
Automatic speaker verification (ASV) is one of the core technologies in biometric identification. With the ubiquitous usage of ASV systems in safety-critical applications, more and…
Replay and Synthetic Speech Detection with Res2net Architecture
Xu Li, Na Li, Chao Weng +4
Existing approaches for replay and synthetic speech detection still lack generalizability to unseen spoofing attacks. This work proposes to leverage a novel model structure, so-cal…
Investigating Robustness of Adversarial Samples Detection for Automatic Speaker Verification
Xu Li, Na Li, Jinghua Zhong +5
Recently adversarial attacks on automatic speaker verification (ASV) systems attracted widespread attention as they pose severe threats to ASV systems. However, methods to defend a…
Bayesian x-vector: Bayesian Neural Network based x-vector System for Speaker Verification
Xu Li, Jinghua Zhong, Jianwei Yu +4
Speaker verification systems usually suffer from the mismatch problem between training and evaluation data, such as speaker population mismatch, the channel and environment variati…
Deep segmental phonetic posterior-grams based discovery of non-categories in L2 English speech
Xu Li, Xixin Wu, Xunying Liu +1
Second language (L2) speech is often labeled with the native, phone categories. However, in many cases, it is difficult to decide on a categorical phone that an L2 segment belongs…
Adversarial Attacks on GMM i-vector based Speaker Verification Systems
Xu Li, Jinghua Zhong, Xixin Wu +3
This work investigates the vulnerability of Gaussian Mixture Model (GMM) i-vector based speaker verification systems to adversarial attacks, and the transferability of adversarial…