5 citations · 7 across the 8 of their papers we have counts for
8 papers
Speaker Privacy and Security in the Big Data Era: Protection and Defense against Deepfake
Liping Chen, Kong Aik Lee, Zhen-Hua Ling +4
In the era of big data, remarkable advancements have been achieved in personalized speech generation techniques that utilize speaker attributes, including voice and speaking style,…
Interpolating Speaker Identities in Embedding Space for Data Expansion
Tianchi Liu, Ruijie Tao, Qiongqiong Wang +5
The success of deep learning-based speaker verification systems is largely attributed to access to large-scale and diverse speaker identity data. However, collecting data from more…
Nes2Net: A Lightweight Nested Architecture for Foundation Model Driven Speech Anti-spoofing
Tianchi Liu, Duc-Tuan Truong, Rohan Kumar Das +2
Speech foundation models have significantly advanced various speech-related tasks by providing exceptional representation capabilities. However, their high-dimensional output featu…
ExPO: Explainable Phonetic Trait-Oriented Network for Speaker Verification
Yi Ma, Shuai Wang, Tianchi Liu +1
In speaker verification, we use computational method to verify if an utterance matches the identity of an enrolled speaker. This task is similar to the manual task of forensic voic…
How Do Neural Spoofing Countermeasures Detect Partially Spoofed Audio?
Tianchi Liu, Lin Zhang, Rohan Kumar Das +3
Partially manipulating a sentence can greatly change its meaning. Recent work shows that countermeasures (CMs) trained on partially spoofed audio can effectively detect such spoofi…
Gradient weighting for speaker verification in extremely low Signal-to-Noise Ratio
Yi Ma, Kong Aik Lee, Ville Hautamäki +2
Speaker verification is hampered by background noise, particularly at extremely low Signal-to-Noise Ratio (SNR) under 0 dB. It is difficult to suppress noise without introducing un…
Golden Gemini is All You Need: Finding the Sweet Spots for Speaker Verification
Tianchi Liu, Kong Aik Lee, Qiongqiong Wang +1
Previous studies demonstrate the impressive performance of residual neural networks (ResNet) in speaker verification. The ResNet models treat the time and frequency dimensions equa…
Disentangling Voice and Content with Self-Supervision for Speaker Recognition
Tianchi Liu, Kong Aik Lee, Qiongqiong Wang +1
For speaker recognition, it is difficult to extract an accurate speaker representation from speech because of its mixture of speaker traits and content. This paper proposes a disen…