Speaker identification by K-nearest neighbors: Application of PCA and LDA prior to KNN

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F11%3A00323534" target="_blank" >RIV/68407700:21230/11:00323534 - isvavai.cz</a>

Výsledek na webu

<a href="https://ieeexplore.ieee.org/document/5977419" target="_blank" >https://ieeexplore.ieee.org/document/5977419</a>

DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Speaker identification by K-nearest neighbors: Application of PCA and LDA prior to KNN

Popis výsledku v původním jazyce

This article presents the task of speaker identification in a closed group. It discusses main steps of the identification process ranging from the proper speech features to the classification methods and statistical signal processing. However, its main focus is on tuning the final system using KNN classification method by setting up the number of neighbors, and reducing the feature vector dimension by PCA and LDA not only to speed up but possibly improve the overall performance. By selecting eligible number of neighbors a 6% improvement in the recognition was reached. Moreover, application of both PCA and LDA reduced the feature vector dimension by more than 50% while slightly increasing the recognition accuracy.

Název v anglickém jazyce

Speaker identification by K-nearest neighbors: Application of PCA and LDA prior to KNN

Popis výsledku anglicky

This article presents the task of speaker identification in a closed group. It discusses main steps of the identification process ranging from the proper speech features to the classification methods and statistical signal processing. However, its main focus is on tuning the final system using KNN classification method by setting up the number of neighbors, and reducing the feature vector dimension by PCA and LDA not only to speed up but possibly improve the overall performance. By selecting eligible number of neighbors a 6% improvement in the recognition was reached. Moreover, application of both PCA and LDA reduced the feature vector dimension by more than 50% while slightly increasing the recognition accuracy.

Druh

D - Stať ve sborníku

CEP obor

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OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

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Návaznosti

N - Vyzkumna aktivita podporovana z neverejnych zdroju

Rok uplatnění

2011

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

2011 18th International Conference on Systems, Signals and Image Processing

ISBN

978-9958-9966-1-0

ISSN

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e-ISSN

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Počet stran výsledku

4

Strana od-do

295-298

Název nakladatele

University of Sarajevo

Místo vydání

Sarajevo

Místo konání akce

Sarajevo

Datum konání akce

16. 6. 2011

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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