基于LDOF准则的自适应高斯后端语种识别方法

叶中付; 戚婷; 李赛峰; 宋彦

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

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基于LDOF准则的自适应高斯后端语种识别方法

学术论文 | 更新时间：2024-06-05

- 基于LDOF准则的自适应高斯后端语种识别方法
- Adaptive Gaussian back-end based on LDOF criterion for language recognition
- 通信学报 2017年38卷第4期页码：17-24
- 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：
  
  [ "叶中付（1959-），男，安徽桐城人，博士，中国科学技术大学教授、博士生导师，主要研究方向为语音信号处理、阵列信号处理、雷达信号处理和图像分析与处理。" ]
  [ "戚婷（1993-），女，安徽淮南人，中国科学技术大学硕士生，主要研究方向为语种识别。" ]
  [ "李赛峰（1980-），男，江西萍乡人，中国科学技术大学博士生，主要研究方向为通信信号处理和语音信号处理。" ]
  [ "宋彦（1972-），男，安徽合肥人，博士，中国科学技术大学副教授，主要研究方向为语种识别和基于内容的音/视频分析与检索。" ]
- 基金信息：
  
  数学工程与先进计算国家重点实验室开放基金资助项目(2015A15)
- DOI：10.11959/j.issn.1000-436x.2017096
  中图分类号： TN912.34
- 网络出版日期：2017-04，
  
  纸质出版日期：2017-04-25
- 稿件说明：
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叶中付, 戚婷, 李赛峰, 等. 基于LDOF准则的自适应高斯后端语种识别方法[J]. 通信学报, 2017,38(4):17-24.

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.
叶中付, 戚婷, 李赛峰, 等. 基于LDOF准则的自适应高斯后端语种识别方法[J]. 通信学报, 2017,38(4):17-24. DOI： 10.11959/j.issn.1000-436x.2017096.

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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