G-IFT: A Gated Linear Unit adapter with Iterative Fine-Tuning for Low-Resource Children's Speaker Verification

• URL: http://arxiv.org/abs/2508.07836v1
• Date: Mon, 11 Aug 2025 10:41:56 GMT
• Title: G-IFT: A Gated Linear Unit adapter with Iterative Fine-Tuning for Low-Resource Children's Speaker Verification
• Authors: Vishwas M. Shetty, Jiusi Zheng, Abeer Alwan,
• Abstract summary: We propose an innovative framework, a Gated Linear Unit adapter with Iterative Fine-Tuning (G-IFT)<n>In this paper, we propose 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 children's speech domain.
• Score: 18.19235178193197
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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 children'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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