# openFEAT: Improving Speaker Identification by Open-set Few-shot Embedding Adaptation with Transformer

arXiv: https://arxiv.org/abs/2202.12349 · HTML: https://mlsanity.com/paper/2202.12349/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Feb 2022.

## Built on by (most cited first, 3 of 3)

- [Towards Few-Shot Learning in the Open World: A Review and Beyond](https://mlsanity.com/paper/2408.09722/) (2024)
- [Few-Shot Open-Set Audio Classification Using Attention Information-Fused Prototypes](https://mlsanity.com/paper/2607.01297/) (2026)
- [Graph-based Multi-View Fusion and Local Adaptation: Mitigating Within-Household Confusability for Speaker Identification](https://mlsanity.com/paper/2207.04081/) (2022)

## Abstract (arXiv)

Household speaker identification with few enrollment utterances is an important yet challenging problem, especially when household members share similar voice characteristics and room acoustics. A common embedding space learned from a large number of speakers is not universally applicable for the optimal identification of every speaker in a household. In this work, we first formulate household speaker identification as a few-shot open-set recognition task and then propose a novel embedding adaptation framework to adapt speaker representations from the given universal embedding space to a household-specific embedding space using a set-to-set function, yielding better household speaker identification performance. With our algorithm, Open-set Few-shot Embedding Adaptation with Transformer (openFEAT), we observe that the speaker identification equal error rate (IEER) on simulated households with 2 to 7 hard-to-discriminate speakers is reduced by 23% to 31% relative.