# Deep multi-metric learning for text-independent speaker verification

arXiv: https://arxiv.org/abs/2007.10479 · HTML: https://mlsanity.com/paper/2007.10479/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Jul 2020.

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

- [CS-Rep: Making Speaker Verification Networks Embracing Re-parameterization](https://mlsanity.com/paper/2110.13465/) (2021)
- [TMS: A Temporal Multi-scale Backbone Design for Speaker Embedding](https://mlsanity.com/paper/2203.09098/) (2022)

## Abstract (arXiv)

Text-independent speaker verification is an important artificial intelligence problem that has a wide spectrum of applications, such as criminal investigation, payment certification, and interest-based customer services. The purpose of text-independent speaker verification is to determine whether two given uncontrolled utterances originate from the same speaker or not. Extracting speech features for each speaker using deep neural networks is a promising direction to explore and a straightforward solution is to train the discriminative feature extraction network by using a metric learning loss function. However, a single loss function often has certain limitations. Thus, we use deep multi-metric learning to address the problem and introduce three different losses for this problem, i.e., triplet loss, n-pair loss and angular loss. The three loss functions work in a cooperative way to train a feature extraction network equipped with Residual connections and squeeze-and-excitation attention. We conduct experiments on the large-scale textttVoxCeleb2 dataset, which contains over a million utterances from over 6,000 speakers, and the proposed deep neural network obtains an equal error rate of 3.48%, which is a very competitive result. Codes for both training and testing and pretrained models are available at urlhttps://github.com/GreatJiweix/DmmlTiSV, which is the first publicly available code repository for large-scale text-independent speaker verification with performance on par with the state-of-the-art systems.