# Improved Deep Speaker Feature Learning for Text-Dependent Speaker Recognition

arXiv: https://arxiv.org/abs/1506.08349 · HTML: https://mlsanity.com/paper/1506.08349/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Jun 2015.

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

- [Wake-Cough: cough spotting and cougher identification for personalised long-term cough monitoring](https://mlsanity.com/paper/2110.03771/) (2021)
- [Tackling Spoofing-Aware Speaker Verification with Multi-Model Fusion](https://mlsanity.com/paper/2206.09131/) (2022)

## Abstract (arXiv)

A deep learning approach has been proposed recently to derive speaker identifies (d-vector) by a deep neural network (DNN). This approach has been applied to text-dependent speaker recognition tasks and shows reasonable performance gains when combined with the conventional i-vector approach. Although promising, the existing d-vector implementation still can not compete with the i-vector baseline. This paper presents two improvements for the deep learning approach: a phonedependent DNN structure to normalize phone variation, and a new scoring approach based on dynamic time warping (DTW). Experiments on a text-dependent speaker recognition task demonstrated that the proposed methods can provide considerable performance improvement over the existing d-vector implementation.