# Deep Speaker Feature Learning for Text-independent Speaker Verification

arXiv: https://arxiv.org/abs/1705.03670 · HTML: https://mlsanity.com/paper/1705.03670/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 May 2017.

## Built on by (most cited first, 6 of 6)

- [Voting for the right answer: Adversarial defense for speaker verification](https://mlsanity.com/paper/2106.07868/) (2021)
- [CN-Celeb-AV: A Multi-Genre Audio-Visual Dataset for Person Recognition](https://mlsanity.com/paper/2305.16049/) (2023)
- [Tackling Spoofing-Aware Speaker Verification with Multi-Model Fusion](https://mlsanity.com/paper/2206.09131/) (2022)
- [Adversarial Data Augmentation for Robust Speaker Verification](https://mlsanity.com/paper/2402.02699/) (2024)
- [Privacy-Utility Balanced Voice De-Identification Using Adversarial Examples](https://mlsanity.com/paper/2211.05446/) (2022)
- [Ordered and Binary Speaker Embedding](https://mlsanity.com/paper/2305.16043/) (2023)

## Abstract (arXiv)

Recently deep neural networks (DNNs) have been used to learn speaker features. However, the quality of the learned features is not sufficiently good, so a complex back-end model, either neural or probabilistic, has to be used to address the residual uncertainty when applied to speaker verification, just as with raw features. This paper presents a convolutional time-delay deep neural network structure (CT-DNN) for speaker feature learning. Our experimental results on the Fisher database demonstrated that this CT-DNN can produce high-quality speaker features: even with a single feature (0.3 seconds including the context), the EER can be as low as 7.68%. This effectively confirmed that the speaker trait is largely a deterministic short-time property rather than a long-time distributional pattern, and therefore can be extracted from just dozens of frames.