Beyond Short Segments : Expanding Speaker Embeddings with Vector Archives
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 se...
| Authors | Hyunku Kang, Minkyu Cho, Chanwoo Kim |
|---|---|
| Primary category | eess.AS |
| Published | 26 July 2026 |
| Last updated at source | 26 July 2026 |
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