.png){kind=icon}
返回
Text-Dependent Speaker Recognition With Random Digit Strings
.png){kind=icon}
PRE
AI
DOI：10.1109/TASLP.2016.2546458.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
In this paper, we explore joint factor analysis (JFA) for text-dependent speaker recognition with random digit strings. The core of the proposed method is a JFA model by which we extract features. These features can either represent overall utterances or individual digits, and are fed into a trainable backend to estimate likelihood ratios. Within this framework, several extensions are proposed. First is a logistic regression method for combining log-likelihood ratios that correspond to individual mixture components. Second is the extraction of phonetically aware Baum-Welch statistics, by using forced alignment instead of the typical posterior probabilities that are derived by the universal background model. We also explore a digit-string-dependent way to apply score normalization that exhibits a notable improvement compared to the standard one. By fusing six JFA features, we attained 2.01% and 3.19% equal error rates on male and female, respectively, on the challenging RSR2015 (part III) dataset.
Keyword:
Joint factor analysis
text-dependent speaker recognition
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
I
IF:
5.1
论文数:
2.6K
被引数:
1.1W
机构
暂无机构信息
引用论文
Selectivity and weed control efficacy of some herbicides applied to sprinkler irrigated rice (Oryza sativa L.)选择性及某些用于喷灌水稻（Oryza sativa L.）的除草剂除草效果
OAAI
.png){kind=icon}
OAAI分享收藏Modeling of Transient Transport of Soluble Proteins in the Connecting Cilium of a Photoreceptor Cell
PREAI
分享收藏
查看更多内容