7 citations · 14 across the 9 of their papers we have counts for
9 papers
MeMo: Attentional Momentum for Real-Time Audio-Visual Target Speaker Extraction Under Impaired Visual Conditions
Junjie Li, Wenxuan Wu, Shuai Wang +4
Audio-visual Target Speaker Extraction (AV-TSE) aims to isolate a target speaker's voice from multi-speaker environments by leveraging visual cues as guidance. However, the perform…
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…
MoMuSE: Momentum Multi-modal Target Speaker Extraction for Real-time Scenarios with Impaired Visual Cues
Junjie Li, Ke Zhang, Shuai Wang +3
Audio-visual Target Speaker Extraction (AV-TSE) aims to isolate the speech of a specific target speaker from an audio mixture using time-synchronized visual cues. In real-world sce…
Towards Quantifying and Reducing Language Mismatch Effects in Cross-Lingual Speech Anti-Spoofing
Tianchi Liu, Ivan Kukanov, Zihan Pan +3
The effects of language mismatch impact speech anti-spoofing systems, while investigations and quantification of these effects remain limited. Existing anti-spoofing datasets are m…
Overview of Speaker Modeling and Its Applications: From the Lens of Deep Speaker Representation Learning
Shuai Wang, Zhengyang Chen, Kong Aik Lee +2
Speaker individuality information is among the most critical elements within speech signals. By thoroughly and accurately modeling this information, it can be utilized in various i…
Cosine Scoring with Uncertainty for Neural Speaker Embedding
Qiongqiong Wang, Kong Aik Lee
Uncertainty modeling in speaker representation aims to learn the variability present in speech utterances. While the conventional cosine-scoring is computationally efficient and pr…
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…