# Baselines and Protocols for Household Speaker Recognition

arXiv: https://arxiv.org/abs/2205.00288 · HTML: https://mlsanity.com/paper/2205.00288/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Apr 2022.

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

- [Probabilistic Back-ends for Online Speaker Recognition and Clustering](https://mlsanity.com/paper/2302.09523/) (2023)
- [Distilling Multi-Level X-vector Knowledge for Small-footprint Speaker Verification](https://mlsanity.com/paper/2303.01125/) (2023)
- [Dynamic Recognition of Speakers for Consent Management by Contrastive Embedding Replay](https://mlsanity.com/paper/2205.08459/) (2022)

## Abstract (arXiv)

Speaker recognition on household devices, such as smart speakers, features several challenges: (i) robustness across a vast number of heterogeneous domains (households), (ii) short utterances, (iii) possibly absent speaker labels of the enrollment data (passive enrollment), and (iv) presence of unknown persons (guests). While many commercial products exist, there is less published research and no publicly-available evaluation protocols or open-source baselines. Our work serves to bridge this gap by providing an accessible evaluation benchmark derived from public resources (VoxCeleb and ASVspoof 2019 data) along with a preliminary pool of open-source baselines. This includes four algorithms for active enrollment (speaker labels available) and one algorithm for passive enrollment.