Beyond Short Segments: Expanding Speaker Embeddings with Vector Archives

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.

Audio and Speech Processing (eess.AS); Computation and Language (cs.CL); Sound (cs.SD)

arXiv:2609.25007 [eess.AS]

(or arXiv:2609.25007v1 [eess.AS] for this version)

https://doi.org/10.48550/arXiv.2609.25007 Focus to learn more arXiv-issued DOI via DataCite

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