5 citations · 10 across the 19 of their papers we have counts for
12 papers · 1 filter
PhiNet: Speaker Verification with Phonetic Interpretability
Yi Ma, Shuai Wang, Tianchi Liu +1
Despite remarkable progress, automatic speaker verification (ASV) systems typically lack the transparency required for high-accountability applications. Motivated by how human expe…
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…
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…
Speech Foundation Model Ensembles for the Controlled Singing Voice Deepfake Detection (CtrSVDD) Challenge 2024
Anmol Guragain, Tianchi Liu, Zihan Pan +2
This work details our approach to achieving a leading system with a 1.79% pooled equal error rate (EER) on the evaluation set of the Controlled Singing Voice Deepfake Detection (Ct…
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…
Voice Conversion Augmentation for Speaker Recognition on Defective Datasets
Ruijie Tao, Zhan Shi, Yidi Jiang +2
Modern speaker recognition system relies on abundant and balanced datasets for classification training. However, diverse defective datasets, such as partially-labelled, small-scale…
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…
Incorporating Uncertainty from Speaker Embedding Estimation to Speaker Verification
Qiongqiong Wang, Kong Aik Lee, Tianchi Liu
Speech utterances recorded under differing conditions exhibit varying degrees of confidence in their embedding estimates, i.e., uncertainty, even if they are extracted using the sa…
Scoring of Large-Margin Embeddings for Speaker Verification: Cosine or PLDA?
Qiongqiong Wang, Kong Aik Lee, Tianchi Liu
The emergence of large-margin softmax cross-entropy losses in training deep speaker embedding neural networks has triggered a gradual shift from parametric back-ends to a simpler c…
Speaker-Utterance Dual Attention for Speaker and Utterance Verification
Tianchi Liu, Rohan Kumar Das, Maulik Madhavi +2
In this paper, we study a novel technique that exploits the interaction between speaker traits and linguistic content to improve both speaker verification and utterance verificatio…