Adaptive Gaussian back-end based on LDOF criterion for language recognition

Zhong-fu YE; Ting QI; Sai-feng LI; Yan SONG

doi:10.11959/j.issn.1000-436x.2017096

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Adaptive Gaussian back-end based on LDOF criterion for language recognition

Papers | 更新时间：2024-06-05

- Adaptive Gaussian back-end based on LDOF criterion for language recognition
- Journal on Communications Vol. 38, Issue 4, Pages: 17-24(2017)
- 作者机构：
  
  1. 中国科学技术大学信息科学技术学院，安徽合肥 230027
  2. 中国科学技术大学语音及语言信息处理国家工程实验室，安徽合肥 230027
  3. 数学工程与先进计算国家重点实验室，江苏无锡 214125
- 作者简介：
- 基金信息：
  
  The Open Project Program of the State Key Laboratory of Mathematical Engineering and Advanced Computing(2015A15)
- DOI：10.11959/j.issn.1000-436x.2017096
  CLC： TN912.34
- Online First：2017-04，
  
  Published：25 April 2017
- 稿件说明：
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Zhong-fu YE, Ting QI, Sai-feng LI, et al. Adaptive Gaussian back-end based on LDOF criterion for language recognition[J]. Journal on Communications, 2017, 38(4): 17-24.
DOI：

Zhong-fu YE, Ting QI, Sai-feng LI, et al. Adaptive Gaussian back-end based on LDOF criterion for language recognition[J]. Journal on Communications, 2017, 38(4): 17-24. DOI： 10.11959/j.issn.1000-436x.2017096.

摘要

针对由语种类内多样性引起的测试样本和训练模型不匹配的问题，提出一种基于局部距离离群因子准则（LDOF

local distance-based outlier factor）的自适应高斯后端语种识别方法。定义LDOF准则，实现有效的参数寻优过程并动态地在多类语种训练集上挑选出与测试样本特性相近的训练样本，调整原高斯后端，进而得到改进的语种识别方法。在NIST LRE 2009的6个易混淆语种任务集上的实验结果表明，所提方法的等错误概率（EER

equal error rate）和平均检测代价有显著提升。

Abstract

In order to alleviate the mismatch in model between training and testing samples caused by inter-language variations

adaptive Gaussian back-end based on LDOF criterion was proposed for language recognition.The local distance-based outlier factor (LDOF) criterion was defined to find the appropriate model parameters and dynamically select the training data subset similar to the testing samples from multiple class training sets.Then original back-end was adjusted to obtain a more matched recognition model.Experimental results on NIST LRE 2009 easily-confused language data set show that proposed method achieves an obvious performance improvement on both the equal error rate (ERR) and average decision cost function.

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references

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