93 citations · 122 across the 6 of their papers we have counts for
6 papers
Improving speaker verification robustness with synthetic emotional utterances
Nikhil Kumar Koditala, Chelsea Jui-Ting Ju, Ruirui Li +3
A speaker verification (SV) system offers an authentication service designed to confirm whether a given speech sample originates from a specific speaker. This technology has paved…
Adversarial Reweighting for Speaker Verification Fairness
Minho Jin, Chelsea J. -T. Ju, Zeya Chen +3
We address performance fairness for speaker verification using the adversarial reweighting (ARW) method. ARW is reformulated for speaker verification with metric learning, and show…
openFEAT: Improving Speaker Identification by Open-set Few-shot Embedding Adaptation with Transformer
Kishan K C, Zhenning Tan, Long Chen +4
Household speaker identification with few enrollment utterances is an important yet challenging problem, especially when household members share similar voice characteristics and r…
Contrastive-mixup learning for improved speaker verification
Xin Zhang, Minho Jin, Roger Cheng +3
This paper proposes a novel formulation of prototypical loss with mixup for speaker verification. Mixup is a simple yet efficient data augmentation technique that fabricates a weig…
Learning and Evaluating Representations for Deep One-class Classification
Kihyuk Sohn, Chun-Liang Li, Jinsung Yoon +2
We present a two-stage framework for deep one-class classification. We first learn self-supervised representations from one-class data, and then build one-class classifiers on lear…
A Streaming On-Device End-to-End Model Surpassing Server-Side Conventional Model Quality and Latency
Tara N. Sainath, Yanzhang He, Bo Li +26
Thus far, end-to-end (E2E) models have not been shown to outperform state-of-the-art conventional models with respect to both quality, i.e., word error rate (WER), and latency, i.e…