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Title:Analiza netekočnosti v govorjenem jeziku s strojnim učenjem : magistrsko delo
Authors:ID Rantuša, Lara (Author)
ID Verdonik, Darinka (Mentor) More about this mentor... New window
ID Karakatič, Sašo (Comentor)
Files:.pdf MAG_Rantusa_Lara_2026.pdf (1,19 MB)
MD5: BC118883D7CEEB221F2C8C9345DD275F
 
Language:Slovenian
Work type:Master's thesis/paper
Typology:2.09 - Master's Thesis
Organization:FERI - Faculty of Electrical Engineering and Computer Science
Abstract:Magistrsko delo obravnava analizo govorne netekočnosti v govorjeni komunikaciji z uporabo metod strojnega učenja. Namen raziskave je bil preučiti pojavnost in značilnosti netekočnosti ter ovrednotiti možnost njihove samodejne prepoznave z uporabo akustičnih značilk. Delo se umešča na področje obdelave naravnega jezika in govorne tehnologije. Uporabljene so bile metode ekstrakcije značilk ter modeli, kot so klasifikator z naključnimi gozdovi, skriti modeli Markova in nevronske mreže. Rezultati kažejo, da je netekočnosti mogoče prepoznati z zadovoljivo natančnostjo, vendar je pri interpretaciji potrebna previdnost.
Keywords:govorne netekočnosti, strojno učenje, obdelava govora
Place of publishing:Maribor
Place of performance:Maribor
Publisher:[L. Rantuša]
Year of publishing:2026
Number of pages:1 spletni vir (1 datoteka PDF (IX, 80 str.))
PID:20.500.12556/DKUM-97623 New window
UDC:004.934.2:004.85(043.2)
COBISS.SI-ID:278308611 New window
Publication date in DKUM:08.05.2026
Views:165
Downloads:22
Metadata:XML DC-XML DC-RDF
Categories:KTFMB - FERI
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Licences

License:CC BY-NC 4.0, Creative Commons Attribution-NonCommercial 4.0 International
Link:http://creativecommons.org/licenses/by-nc/4.0/
Description:A creative commons license that bans commercial use, but the users don’t have to license their derivative works on the same terms.
Licensing start date:25.03.2026

Secondary language

Language:English
Title:Disfluency analysis in spoken language with machine learning
Abstract:This master's thesis explores speech disfluencies in spontaneous speech and their automatic detection using machine learning methods. The aim of the study was to analyze the occurrence of disfluencies and evaluate whether they can be successfully detected based on acoustic features. The research belongs to the field of natural language processing and speech technology. Feature extraction methods and models such as Random Forest Classifier, Hidden Markov Models and neural networks were applied. The results show that disfluencies can be detected with satisfactory accuracy; however, careful interpretation of the findings is required.
Keywords:speech disfluencies, machine learning, speech processing


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