Judgement of unvoiced and voiced pronunciation based on sparse feature with DCT dictionary

The judgment of unvoiced and voiced sound based on sparse representation in DCT dictionary is implemented. Human pronunciation can be mainly divided into unvoiced and voiced sound. Sparse representation can represent the signal with as few coefficients as possible on a set of over-complete vectors,...

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Main Authors: Wang Lianzi, Mastorakis Nikos, Zhuang Xiaodong
Format: Article
Language:English
Published: EDP Sciences 2019-01-01
Series:MATEC Web of Conferences
Online Access:https://www.matec-conferences.org/articles/matecconf/pdf/2019/41/matecconf_cscc2019_04012.pdf
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author Wang Lianzi
Mastorakis Nikos
Zhuang Xiaodong
author_facet Wang Lianzi
Mastorakis Nikos
Zhuang Xiaodong
author_sort Wang Lianzi
collection DOAJ
description The judgment of unvoiced and voiced sound based on sparse representation in DCT dictionary is implemented. Human pronunciation can be mainly divided into unvoiced and voiced sound. Sparse representation can represent the signal with as few coefficients as possible on a set of over-complete vectors, which can reveal the most representative features of signals. In this paper, the difference between the sparse representation of unvoiced and voiced sound is studied, based on which a method is proposed to distinguish unvoiced and voiced sound in words. The experimental results prove that the proposed method is effective.
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spelling doaj.art-e1f8dc97ffdc41aeaf1a2ea97c8e7fe52022-12-21T17:22:23ZengEDP SciencesMATEC Web of Conferences2261-236X2019-01-012920401210.1051/matecconf/201929204012matecconf_cscc2019_04012Judgement of unvoiced and voiced pronunciation based on sparse feature with DCT dictionaryWang Lianzi0Mastorakis Nikos1Zhuang Xiaodong21College of Electronics and Information, Qingdao University2Technical University of Sofia1College of Electronics and Information, Qingdao UniversityThe judgment of unvoiced and voiced sound based on sparse representation in DCT dictionary is implemented. Human pronunciation can be mainly divided into unvoiced and voiced sound. Sparse representation can represent the signal with as few coefficients as possible on a set of over-complete vectors, which can reveal the most representative features of signals. In this paper, the difference between the sparse representation of unvoiced and voiced sound is studied, based on which a method is proposed to distinguish unvoiced and voiced sound in words. The experimental results prove that the proposed method is effective.https://www.matec-conferences.org/articles/matecconf/pdf/2019/41/matecconf_cscc2019_04012.pdf
spellingShingle Wang Lianzi
Mastorakis Nikos
Zhuang Xiaodong
Judgement of unvoiced and voiced pronunciation based on sparse feature with DCT dictionary
MATEC Web of Conferences
title Judgement of unvoiced and voiced pronunciation based on sparse feature with DCT dictionary
title_full Judgement of unvoiced and voiced pronunciation based on sparse feature with DCT dictionary
title_fullStr Judgement of unvoiced and voiced pronunciation based on sparse feature with DCT dictionary
title_full_unstemmed Judgement of unvoiced and voiced pronunciation based on sparse feature with DCT dictionary
title_short Judgement of unvoiced and voiced pronunciation based on sparse feature with DCT dictionary
title_sort judgement of unvoiced and voiced pronunciation based on sparse feature with dct dictionary
url https://www.matec-conferences.org/articles/matecconf/pdf/2019/41/matecconf_cscc2019_04012.pdf
work_keys_str_mv AT wanglianzi judgementofunvoicedandvoicedpronunciationbasedonsparsefeaturewithdctdictionary
AT mastorakisnikos judgementofunvoicedandvoicedpronunciationbasedonsparsefeaturewithdctdictionary
AT zhuangxiaodong judgementofunvoicedandvoicedpronunciationbasedonsparsefeaturewithdctdictionary