Text-Independent Speaker Verification Based on Deep Neural Networks and Segmental Dynamic TimeWarping

被引：0
|
作者：
Adel, Mohamed [1 ]
Afify, Mohamed [1 ]
Gaballah, Akram [2 ]
Fayek, Magda [3 ]
机构：
[1] Microsoft Adv Technol Lab, Cairo, Egypt
[2] Microsoft Corp, Redmond, WA 98052 USA
[3] Cairo Univ, Giza, Egypt
来源：
2018 IEEE WORKSHOP ON SPOKEN LANGUAGE TECHNOLOGY (SLT 2018) | 2018年
关键词：
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
In this paper we present a new method for text-independent speaker verification that combines segmental dynamic time warping (SDTW) and the d-vector approach. The d-vectors, generated from a feed forward deep neural network trained to distinguish between speakers, are used as features to perform alignment and hence calculate the overall distance between the enrolment and test utterances. We present results on the NIST 2008 data set for speaker verification where the proposed method outperforms the conventional i-vector baseline with PLDA scores and outperforms d-vector approach with local distances based on cosine and PLDA scores. Also score combination with the i-vector/ PLDA baseline leads to significant gains over both methods.
引用
收藏
页码：1001 / 1006
页数：6
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