# Deep Speaker Vectors for Semi Text-independent Speaker Verification

arXiv: https://arxiv.org/abs/1505.06427 · HTML: https://mlsanity.com/paper/1505.06427/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 May 2015.

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

- [Novel Hybrid DNN Approaches for Speaker Verification in Emotional and Stressful Talking Environments](https://mlsanity.com/paper/2112.13353/) (2021)
- [Shushing! Let's Imagine an Authentic Speech from the Silent Video](https://mlsanity.com/paper/2503.14928/) (2025)

## Abstract (arXiv)

Recent research shows that deep neural networks (DNNs) can be used to extract deep speaker vectors (d-vectors) that preserve speaker characteristics and can be used in speaker verification. This new method has been tested on text-dependent speaker verification tasks, and improvement was reported when combined with the conventional i-vector method. This paper extends the d-vector approach to semi text-independent speaker verification tasks, i.e., the text of the speech is in a limited set of short phrases. We explore various settings of the DNN structure used for d-vector extraction, and present a phone-dependent training which employs the posterior features obtained from an ASR system. The experimental results show that it is possible to apply d-vectors on semi text-independent speaker recognition, and the phone-dependent training improves system performance.