# Beyond Short Segments : Expanding Speaker Embeddings with Vector Archives

arXiv: https://arxiv.org/abs/2609.25007 · HTML: https://mlsanity.com/paper/2609.25007/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 28 of its 40 references that are in the map.
- First posted on arXiv 26 Jul 2026.

## Builds on (most cited first, 28 of 28)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks](https://mlsanity.com/paper/1703.03400/) (2017)
- [Squeeze-and-Excitation Networks](https://mlsanity.com/paper/1709.01507/) (2017)
- [wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations](https://mlsanity.com/paper/2006.11477/) (2020)
- [HuBERT: Self-Supervised Speech Representation Learning by Masked Prediction of Hidden Units](https://mlsanity.com/paper/2106.07447/) (2021)
- [WavLM: Large-Scale Self-Supervised Pre-Training for Full Stack Speech Processing](https://mlsanity.com/paper/2110.13900/) (2021)
- [VoxCeleb2: Deep Speaker Recognition](https://mlsanity.com/paper/1806.05622/) (2018)
- [SUPERB: Speech processing Universal PERformance Benchmark](https://mlsanity.com/paper/2105.01051/) (2021)
- [Neural Turing Machines](https://mlsanity.com/paper/1410.5401/) (2014)
- [ECAPA-TDNN: Emphasized Channel Attention, Propagation and Aggregation in TDNN Based Speaker Verification](https://mlsanity.com/paper/2005.07143/) (2020)
- [Res2Net: A New Multi-scale Backbone Architecture](https://mlsanity.com/paper/1904.01169/) (2019)
- [MUSAN: A Music, Speech, and Noise Corpus](https://mlsanity.com/paper/1510.08484/) (2015)
- [Generalized End-to-End Loss for Speaker Verification](https://mlsanity.com/paper/1710.10467/) (2017)
- [Additive Margin Softmax for Face Verification](https://mlsanity.com/paper/1801.05599/) (2018)
- [Attentive Statistics Pooling for Deep Speaker Embedding](https://mlsanity.com/paper/1803.10963/) (2018)
- [Exploring wav2vec 2.0 on speaker verification and language identification](https://mlsanity.com/paper/2012.06185/) (2020)
- [ICASSP 2023 Deep Noise Suppression Challenge](https://mlsanity.com/paper/2303.11510/) (2023)
- [Speaker Recognition Based on Deep Learning: An Overview](https://mlsanity.com/paper/2012.00931/) (2020)
- [Utterance-level Aggregation For Speaker Recognition In The Wild](https://mlsanity.com/paper/1902.10107/) (2019)
- [Exploring the Encoding Layer and Loss Function in End-to-End Speaker and Language Recognition System](https://mlsanity.com/paper/1804.05160/) (2018)
- [Why does Self-Supervised Learning for Speech Recognition Benefit Speaker Recognition?](https://mlsanity.com/paper/2204.12765/) (2022)
- [The IDLAB VoxSRC-20 Submission: Large Margin Fine-Tuning and Quality-Aware Score Calibration in DNN Based Speaker Verification](https://mlsanity.com/paper/2010.11255/) (2020)
- [Self-Supervised Learning with Cluster-Aware-DINO for High-Performance Robust Speaker Verification](https://mlsanity.com/paper/2304.05754/) (2023)
- [Efficient Implementation of the Room Simulator for Training Deep Neural Network Acoustic Models](https://mlsanity.com/paper/1712.03439/) (2017)
- [Meta-Learning for Short Utterance Speaker Recognition with Imbalance Length Pairs](https://mlsanity.com/paper/2004.02863/) (2020)
- [Frame-level speaker embeddings for text-independent speaker recognition and analysis of end-to-end model](https://mlsanity.com/paper/1809.04437/) (2018)
- [Segment Aggregation for short utterances speaker verification using raw waveforms](https://mlsanity.com/paper/2005.03329/) (2020)
- [Length- and Noise-aware Training Techniques for Short-utterance Speaker Recognition](https://mlsanity.com/paper/2008.12218/) (2020)

## Built on by (most cited first, 1 of 1)

- [SISER: Speaker-Invariant Speech Emotion Recognition with Entropy-Based Adversarial Training](https://mlsanity.com/paper/2609.02941/) (2026)

## Abstract (arXiv)

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.