Voiced/Unvoiced Pronunciation Judgement Based on Sparse Representation and Learning Dictionary

被引：1

作者：

Wang, Lianzi ^{[1
]}

Li, Zhongxiao ^{[1
]}

Zhuang, Xiaodong ^{[1
]}

Mastorakis, Nikos E. ^{[2
]}

机构：

[1] Qingdao Univ, Elect & Informat Coll, Qingdao, Peoples R China

[2] Hellen Naval Acad, Elect Engn & Comp Sci Dept, Piraeus, Greece

来源：

2019 3RD EUROPEAN CONFERENCE ON ELECTRICAL ENGINEERING AND COMPUTER SCIENCE (EECS 2019) | 2019年

基金：

美国国家科学基金会;

关键词：

K-SVD; dictionary learning; sparse representation; voiced sound; unvoiced sound; K-SVD; ALGORITHM;

D O I：

10.1109/EECS49779.2019.00038

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

According to the difference between voiced and unvoiced sounds, a new method has been proposed to judge them in this paper. The study selects enough voiced and unvoiced sounds as the object of dictionary learning and employs K-SVD algorithm to construct voiced dictionary and unvoiced dictionary respectively. Then the signals to be judged are sparse represented in voiced dictionary and unvoiced dictionary. The way to distinguish the voiced and unvoiced sounds is comparing the sparsity of coefficients in two dictionaries and the results have proved the effectiveness of this method

引用

页码：147 / 150

页数：4

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