# Graph-based Multi-View Fusion and Local Adaptation: Mitigating Within-Household Confusability for Speaker Identification

arXiv: https://arxiv.org/abs/2207.04081 · HTML: https://mlsanity.com/paper/2207.04081/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 27 references that are in the map.
- First posted on arXiv 8 Jul 2022.

## Builds on (most cited first, 12 of 12)

- [Joint Face Detection and Alignment using Multi-task Cascaded Convolutional Networks](https://mlsanity.com/paper/1604.02878/) (2016)
- [VoxCeleb2: Deep Speaker Recognition](https://mlsanity.com/paper/1806.05622/) (2018)
- [VoxCeleb: a large-scale speaker identification dataset](https://mlsanity.com/paper/1706.08612/) (2017)
- [Generalized End-to-End Loss for Speaker Verification](https://mlsanity.com/paper/1710.10467/) (2017)
- [Fairness in Recommendation Ranking through Pairwise Comparisons](https://mlsanity.com/paper/1903.00780/) (2019)
- [Audio-visual Speaker Recognition with a Cross-modal Discriminative Network](https://mlsanity.com/paper/2008.03894/) (2020)
- [A Multi-View Approach To Audio-Visual Speaker Verification](https://mlsanity.com/paper/2102.06291/) (2021)
- [Improving fairness in speaker verification via Group-adapted Fusion Network](https://mlsanity.com/paper/2202.11323/) (2022)
- [Generalized Matrix Means for Semi-Supervised Learning with Multilayer Graphs](https://mlsanity.com/paper/1910.13951/) (2019)
- [Graph-based Label Propagation for Semi-Supervised Speaker Identification](https://mlsanity.com/paper/2106.08207/) (2021)
- [Improving Speaker Identification for Shared Devices by Adapting Embeddings to Speaker Subsets](https://mlsanity.com/paper/2109.02576/) (2021)
- [openFEAT: Improving Speaker Identification by Open-set Few-shot Embedding Adaptation with Transformer](https://mlsanity.com/paper/2202.12349/) (2022)

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

- [Cross-utterance ASR Rescoring with Graph-based Label Propagation](https://mlsanity.com/paper/2303.15132/) (2023)

## Abstract (arXiv)

Speaker identification (SID) in the household scenario (e.g., for smart speakers) is an important but challenging problem due to limited number of labeled (enrollment) utterances, confusable voices, and demographic imbalances. Conventional speaker recognition systems generalize from a large random sample of speakers, causing the recognition to underperform for households drawn from specific cohorts or otherwise exhibiting high confusability. In this work, we propose a graph-based semi-supervised learning approach to improve household-level SID accuracy and robustness with locally adapted graph normalization and multi-signal fusion with multi-view graphs. Unlike other work on household SID, fairness, and signal fusion, this work focuses on speaker label inference (scoring) and provides a simple solution to realize household-specific adaptation and multi-signal fusion without tuning the embeddings or training a fusion network. Experiments on the VoxCeleb dataset demonstrate that our approach consistently improves the performance across households with different customer cohorts and degrees of confusability.