# Meta-Learning for Short Utterance Speaker Recognition with Imbalance Length Pairs

arXiv: https://arxiv.org/abs/2004.02863 · HTML: https://mlsanity.com/paper/2004.02863/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Apr 2020.

## Built on by (most cited first, 8 of 8)

- [Improved Meta Learning for Low Resource Speech Recognition](https://mlsanity.com/paper/2205.06182/) (2022)
- [Audio Anti-spoofing Using a Simple Attention Module and Joint Optimization Based on Additive Angular Margin Loss and Meta-learning](https://mlsanity.com/paper/2211.09898/) (2022)
- [Improved Relation Networks for End-to-End Speaker Verification and Identification](https://mlsanity.com/paper/2203.17218/) (2022)
- [Toward Improving Synthetic Audio Spoofing Detection Robustness via Meta-Learning and Disentangled Training With Adversarial Examples](https://mlsanity.com/paper/2408.13341/) (2024)
- [Beyond Short Segments : Expanding Speaker Embeddings with Vector Archives](https://mlsanity.com/paper/2609.25007/) (2026)
- [Dynamic Recognition of Speakers for Consent Management by Contrastive Embedding Replay](https://mlsanity.com/paper/2205.08459/) (2022)
- [Meta-Learning Framework for End-to-End Imposter Identification in Unseen Speaker Recognition](https://mlsanity.com/paper/2306.00952/) (2023)
- [DAME: Duration-Aware Matryoshka Embedding for Duration-Robust Speaker Verification](https://mlsanity.com/paper/2601.13999/) (2026)

## Abstract (arXiv)

In practical settings, a speaker recognition system needs to identify a speaker given a short utterance, while the enrollment utterance may be relatively long. However, existing speaker recognition models perform poorly with such short utterances. To solve this problem, we introduce a meta-learning framework for imbalance length pairs. Specifically, we use a Prototypical Networks and train it with a support set of long utterances and a query set of short utterances of varying lengths. Further, since optimizing only for the classes in the given episode may be insufficient for learning discriminative embeddings for unseen classes, we additionally enforce the model to classify both the support and the query set against the entire set of classes in the training set. By combining these two learning schemes, our model outperforms existing state-of-the-art speaker verification models learned with a standard supervised learning framework on short utterance (1-2 seconds) on the VoxCeleb datasets. We also validate our proposed model for unseen speaker identification, on which it also achieves significant performance gains over the existing approaches. The codes are available at https://github.com/seongmin-kye/meta-SR.