# The DKU System for the Speaker Recognition Task of the 2019 VOiCES from a Distance Challenge

arXiv: https://arxiv.org/abs/1907.02194 · HTML: https://mlsanity.com/paper/1907.02194/

## Key facts

- Cited by 1 papers in the map (data through 19 Oct 2026).
- First posted on arXiv 4 Jul 2019.

## Built on by (most cited first, 1 of 1)

- [Task-specific Optimization of Virtual Channel Linear Prediction-based Speech Dereverberation Front-End for Far-Field Speaker Verification](https://mlsanity.com/paper/2112.13569/) (2021)

## Abstract (arXiv)

In this paper, we present the DKU system for the speaker recognition task of the VOiCES from a distance challenge 2019. We investigate the whole system pipeline for the far-field speaker verification, including data pre-processing, short-term spectral feature representation, utterance-level speaker modeling, back-end scoring, and score normalization. Our best single system employs a residual neural network trained with angular softmax loss. Also, the weighted prediction error algorithms can further improve performance. It achieves 0.3668 minDCF and 5.58% EER on the evaluation set by using a simple cosine similarity scoring. Finally, the submitted primary system obtains 0.3532 minDCF and 4.96% EER on the evaluation set.