23.6k citations
- Carnegie Mellon UniversityUS49 papers
- Google (United States)US37 papers
- California Institute of TechnologyUS33 papers
- Amazon (Germany)DE32 papers
- University of Illinois Urbana-ChampaignUS22 papers
- Microsoft (United States)US20 papers
- Stanford UniversityUS20 papers
- The University of Texas at AustinUS20 papers
- University of California, Los AngelesUS20 papers
- University of California, Santa BarbaraUS19 papers
- University of WashingtonUS19 papers
- Georgia Institute of TechnologyUS17 papers
9 papers · 2 filters
Reducing Geographic Disparities in Automatic Speech Recognition via Elastic Weight Consolidation
Viet Anh Trinh, Pegah Ghahremani, Brian King +3
We present an approach to reduce the performance disparity between geographic regions without degrading performance on the overall user population for ASR. A popular approach is to…
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…
Graph-based Multi-View Fusion and Local Adaptation: Mitigating Within-Household Confusability for Speaker Identification
Long Chen, Yixiong Meng, Venkatesh Ravichandran +1
Speaker identification (SID) in the household scenario (e.g., for smart speakers) is an important but challenging problem due to limited number of labeled (enrollment) utterances,…
Learning Representations for New Sound Classes With Continual Self-Supervised Learning
Zhepei Wang, Cem Subakan, Xilin Jiang +4
In this paper, we work on a sound recognition system that continually incorporates new sound classes. Our main goal is to develop a framework where the model can be updated without…
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…
Improving fairness in speaker verification via Group-adapted Fusion Network
Hua Shen, Yuguang Yang, Guoli Sun +4
Modern speaker verification models use deep neural networks to encode utterance audio into discriminative embedding vectors. During the training process, these networks are typical…