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清华大学学报(自然科学版)  2017, Vol. 57 Issue (1): 28-32    DOI: 10.16511/j.cnki.qhdxxb.2017.21.006
  计算机科学与技术 本期目录 | 过刊浏览 | 高级检索 |
基于GMM托肯配比相似度校正得分的说话人识别
杨莹春, 邓立才
浙江大学 计算机学院, 杭州 310027
Score regulation based on GMM token ratio similarity for speaker recognition
YANG Yingchun, DENG Licai
College of Computer Science & Technology, Zhejiang University, Hangzhou 310027, China
全文: PDF(993 KB)  
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摘要 该文提出一种基于Gauss混合模型(GMM)托肯配比相似度校正得分(GMM token ratio similarity based score regulation,GTRSR)的说话人识别方法。基于GMM-UBM(通用背景模型)识别框架,在自适应训练和测试阶段计算并保存自适应训练语句和测试语句在UBM上使特征帧得分最高的Gauss分量编号(GMM token)出现的比例(配比),然后在测试阶段计算测试语句和自适应训练语句的GMM托肯分布的配比的相似度GTRS,当GTRS小于某阈值时对测试得分乘以一个惩罚因子,将结果作为测试语句的最终得分。在MASC数据库上进行的实验表明,该方法能够使系统识别性能有一定的提升。
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杨莹春
邓立才
关键词 说话人识别GMM托肯配比(GTR)得分校正    
Abstract:A GMM token ratio similarity based score regulation approach for speaker recognition is presented in this paper to judge the reliability of a test score based on the GMM token ratio similarity. In the GMM-UBM (universal background model) method, the GMM token which is the index of the UBM component giving the highest score is saved for each frame to form a vector called the GMM token ratio (GTR) of an utterance during the training and testing phases. In the test phase, the test utterance GTR is compared to the training utterance GTR to compute the similarity for a target speaker. When the similarity is less than a threshold, the original likelihood score is regulated by multiplying by a penalty factor as the final score of this test utterance. Tests on MASC show that this method improves the speaker recognition performance.
Key wordsspeaker recognition    GMM token ratio (GTR)    score regulation
收稿日期: 2016-07-05      出版日期: 2017-01-20
ZTFLH:  TP391.43  
引用本文:   
杨莹春, 邓立才. 基于GMM托肯配比相似度校正得分的说话人识别[J]. 清华大学学报(自然科学版), 2017, 57(1): 28-32.
YANG Yingchun, DENG Licai. Score regulation based on GMM token ratio similarity for speaker recognition. Journal of Tsinghua University(Science and Technology), 2017, 57(1): 28-32.
链接本文:  
http://jst.tsinghuajournals.com/CN/10.16511/j.cnki.qhdxxb.2017.21.006  或          http://jst.tsinghuajournals.com/CN/Y2017/V57/I1/28
  图1 基于GMM 托肯配比的得分校正流程图
  表1 方法1、23的EER 及IR
  表2 方法4、5的EER 及IR
  表3 阈值对方法5的影响
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