# Disentangled speaker and nuisance attribute embedding for robust speaker verification

arXiv: https://arxiv.org/abs/2008.03024 · HTML: https://mlsanity.com/paper/2008.03024/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Aug 2020.

## Built on by (most cited first, 2 of 2)

- [UniTTS: Residual Learning of Unified Embedding Space for Speech Style Control](https://mlsanity.com/paper/2106.11171/) (2021)
- [CycleFlow: Purify Information Factors by Cycle Loss](https://mlsanity.com/paper/2110.09928/) (2021)

## Abstract (arXiv)

Over the recent years, various deep learning-based embedding methods have been proposed and have shown impressive performance in speaker verification. However, as in most of the classical embedding techniques, the deep learning-based methods are known to suffer from severe performance degradation when dealing with speech samples with different conditions (e.g., recording devices, emotional states). In this paper, we propose a novel fully supervised training method for extracting a speaker embedding vector disentangled from the variability caused by the nuisance attributes. The proposed framework was compared with the conventional deep learning-based embedding methods using the RSR2015 and VoxCeleb1 dataset. Experimental results show that the proposed approach can extract speaker embeddings robust to channel and emotional variability.