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In speaker verification (SV), the acoustic mismatch between children’s and adults’ speech leads to suboptimal performance when adult-trained SV systems are applied to chil- dren’s speaker verification (C-SV). While domain adaptation techniques can enhance performance on C-SV tasks, they often do so at the expense of significant degradation in performance on adults’ SV (A-SV) tasks. In this study, we propose an Age Agnostic Speaker Verification (AASV) system that achieves robust performance across both C-SV and A-SV tasks. Our approach employs a domain classifier to disentangle age-related attributes from speech and subsequently expands the embedding space using the extracted domain information, forming a unified speaker representation that is robust and highly discriminative across age groups. Experiments on the OGI and Vox- Celeb datasets demonstrate the effectiveness of our approach in bridging SV performance disparities, laying the foundation for inclusive and age-adaptive SV systems.
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This content will become publicly available on August 22, 2026
G-IFT: A Gated Linear Unit adapter with Iterative Fine-Tuning for Low-Resource Children's Speaker Verification
Speaker Verification (SV) systems trained on adults speech often underperform on children’s SV due to the acoustic mismatch, and limited children speech data makes fine-tuning not very effective. In this paper, we propose an innovative framework, a Gated Linear Unit adapter with Iterative Fine-Tuning (G-IFT), to enhance knowledge transfer efficiency between the high-resource adults speech domain and the low-resource chil- dren’s speech domain. In this framework, a Gated Linear Unit adapter is first inserted between the pre-trained speaker embedding model and the classifier. Then the classifier, adapter, and pre-trained speaker embedding model are optimized sequentially in an iterative way. This framework is agnostic to the type of the underlying architecture of the SV system. Our experiments on ECAPA-TDNN, ResNet, and X-vector architectures using the OGI and MyST datasets demonstrate that the G-IFT framework yields consistent reductions in Equal Error Rates compared to baseline methods.
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- Award ID(s):
- 2006979
- PAR ID:
- 10646710
- Publisher / Repository:
- ISCA
- Date Published:
- Page Range / eLocation ID:
- 1 to 5
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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