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DKUM
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Title:
Razvoj lokalnega glasovnega asistenta za upravljanje računalnika v slovenskem jeziku
Authors:
ID
Založnik, Tine
(
Author
)
ID
Mlakar, Uroš
(
Mentor
)
More about this mentor...
Files:
VS_Zaloznik_Tine_2026.pdf
(1,31 MB)
MD5: 3FCDF0094D7785AD1C7A099E105E1B0F
Language:
Slovenian
Work type:
Bachelor thesis/paper
Typology:
2.11 - Undergraduate Thesis
Organization:
FERI - Faculty of Electrical Engineering and Computer Science
Abstract:
V diplomskem delu smo razvili prototip lokalnega glasovnega asistenta za okolje Windows, ki omogoča upravljanje računalnika v slovenščini in deluje brez internetne povezave. S pomočjo modelov Wav2Vec 2.0 in CNN14 smo raziskali uspešnost klasifikacije omejenega nabora glasovnih ukazov, naučenih na posnetkih enega samega govorca. Pokazali smo, da za zanesljivo prepoznavo ukazov in budne besede ne potrebujemo ogromnih količin podatkov, saj smo prepričljive rezultate dosegli že z manjšim naborom lastnih posnetkov.
Keywords:
Prepoznava govora
,
glasovni asistent
,
globoko učenje
Place of publishing:
Maribor
Year of publishing:
2026
PID:
20.500.12556/DKUM-98007
Publication date in DKUM:
29.05.2026
Views:
263
Downloads:
62
Metadata:
Categories:
KTFMB - FERI
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Licences
License:
CC BY-NC-ND 4.0, Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International
Link:
http://creativecommons.org/licenses/by-nc-nd/4.0/
Description:
The most restrictive Creative Commons license. This only allows people to download and share the work for no commercial gain and for no other purposes.
Licensing start date:
10.05.2026
Secondary language
Language:
English
Title:
Development of a local voice assistant for computer management in slovenian
Abstract:
In our thesis, we developed a prototype of a local voice assistant for the Windows environment, which enables computer control in Slovenian and works without an internet connection. Using the Wav2Vec 2.0 and CNN14 models, we investigated the effectiveness of classifying a limited set of voice commands learned from recordings of a single speaker. We showed that we do not need huge amounts of data for reliable command and wake word recognition, as we achieved convincing results with a smaller set of our own recordings.
Keywords:
Speech recognition
,
voice assistant
,
deep learning
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