The DKU System for the Speaker Recognition Task of the 2019 VOiCES from a Distance Challenge
arXiv:1907.02194
Abstract
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.
arXiv commentAccepted by Interspeech 2019
References in corpus (5)
- VoxCeleb2: Deep Speaker Recognition
- VoxCeleb: a large-scale speaker identification dataset
- Pyroomacoustics: A Python package for audio room simulations and array processing algorithms
- The BOSARIS Toolkit: Theory, Algorithms and Code for Surviving the New DCF
- The VOiCES from a Distance Challenge 2019 Evaluation Plan