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arXiv · 2609.25007

Beyond Short Segments : Expanding Speaker Embeddings with Vector Archives

Hyunku Kang·Minkyu Cho·Chanwoo Kim
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Abstract

The performance of state-of-the-art speaker verification (SV) systems severely degrades on short utterances due to insufficient speaker-specific information. To address this critical challenge, we propose the Vector Archive Mapping ECAPA (VAM-ECAPA), a novel system designed to enhance feature extraction from short-duration speech. The core of our system is the Transformer-based Vector Archive Mapping with Statistical Pooling (TVAMSP) module, which enriches information-scarce features by mapping them against a learnable Vector Archive of canonical speaker traits. By integrating the TVAMSP module into a strong WavLM+ECAPA-TDNN baseline, our system learns to map sparse features from short segments into robust, discriminative speaker representations. Experiments on the VoxCeleb1 benchmark show that our proposed VAM-ECAPA achieves a highly competitive EER of 8.334% on 1-second test segments, a 54.8% relative error reduction compared to a conventionally-trained baseline.

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Hyunku Kang, Minkyu Cho, Chanwoo Kim. 2026-07-26. Beyond Short Segments : Expanding Speaker Embeddings with Vector Archives. https://arxiv.org/abs/2609.25007

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