Clustering analysis of phonetic and text feature vectors
Identifikátory výsledku
Kód výsledku v IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216275%3A25530%2F17%3A39911500" target="_blank" >RIV/00216275:25530/17:39911500 - isvavai.cz</a>
Výsledek na webu
<a href="http://dx.doi.org/10.1109/INFORMATICS.2017.8327237" target="_blank" >http://dx.doi.org/10.1109/INFORMATICS.2017.8327237</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/INFORMATICS.2017.8327237" target="_blank" >10.1109/INFORMATICS.2017.8327237</a>
Alternativní jazyky
Jazyk výsledku
angličtina
Název v původním jazyce
Clustering analysis of phonetic and text feature vectors
Popis výsledku v původním jazyce
Our goal is to show an example of using statistical methods to analyse some attributes of speeches. For this purpose, the New Year’s Day speeches of Czech and Czechoslovak presidents are chosen. The aim of our study is researching similarities among these speeches and their recognizability through the history of Czechoslovak politics. All presidents are compared between each other. The comparison method is based on principal component analysis and cluster analysis. Important part is creating a feature vector. The feature vector doesn't have to be the same for successful clustering. There are many varieties and combinations of features that can be selected and used. Correlated variables must be discarded. The most significant features are chosen to represent and characterize the speaker. Some speakers can have something in common according to the chosen features. Or on the other hand they can differ much more from others. This kind of approach can help us to recognize a speech pattern of each spokesman independently.
Název v anglickém jazyce
Clustering analysis of phonetic and text feature vectors
Popis výsledku anglicky
Our goal is to show an example of using statistical methods to analyse some attributes of speeches. For this purpose, the New Year’s Day speeches of Czech and Czechoslovak presidents are chosen. The aim of our study is researching similarities among these speeches and their recognizability through the history of Czechoslovak politics. All presidents are compared between each other. The comparison method is based on principal component analysis and cluster analysis. Important part is creating a feature vector. The feature vector doesn't have to be the same for successful clustering. There are many varieties and combinations of features that can be selected and used. Correlated variables must be discarded. The most significant features are chosen to represent and characterize the speaker. Some speakers can have something in common according to the chosen features. Or on the other hand they can differ much more from others. This kind of approach can help us to recognize a speech pattern of each spokesman independently.
Klasifikace
Druh
D - Stať ve sborníku
CEP obor
—
OECD FORD obor
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Návaznosti výsledku
Projekt
—
Návaznosti
S - Specificky vyzkum na vysokych skolach
Ostatní
Rok uplatnění
2017
Kód důvěrnosti údajů
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Údaje specifické pro druh výsledku
Název statě ve sborníku
Proceeding of 2017 IEEE 14TH International Scientific Conference on Informatics
ISBN
978-1-5386-0888-3
ISSN
—
e-ISSN
neuvedeno
Počet stran výsledku
6
Strana od-do
146-151
Název nakladatele
IEEE (Institute of Electrical and Electronics Engineers)
Místo vydání
New York
Místo konání akce
Poprad
Datum konání akce
14. 11. 2017
Typ akce podle státní příslušnosti
WRD - Celosvětová akce
Kód UT WoS článku
—