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Title:Nenadzorovano učenje akustičnih modelov govora
Authors:ID Donaj, Gregor (Author)
ID Žgank, Andrej (Author)
ID Sepesy Maučec, Mirjam (Author)
Files:.pdf RAZ_Donaj_Gregor_2013.pdf (925,64 KB)
MD5: 7150CC02A7C087635B5BFFAB1A923FCD
 
URL https://journals.um.si/index.php/anali-pazu/article/view/1958
 
Language:English
Work type:Scientific work
Typology:1.01 - Original Scientific Article
Organization:UZUM - University of Maribor Press
Abstract:V članku je predstavljeno nenadzorovano učenje akustičnih modelov za razpoznavanje tekočega govora. Ključna prednost takega učenja je njegova hitrost in nizki stroški v primerjavi z izdelavo transkripcij govora, ki so potrebne za nadzorovano učenje. Predstavljeni sta dve metodi nenadzorovanega učenja, ki sta preizkušeni na razpoznavalniku tekočega govora z velikim slovarjem v domeni dnevno-informativnih oddaj.
Keywords:acoustical models, speech recognition, unsupervised training
Publication status:Published
Publication version:Version of Record
Publication date:01.01.2013
Place of publishing:Maribor
Publisher:Univerza v Mariboru, Univerzitetna založba
Year of publishing:2013
Number of pages:6
Numbering:Letn.3, št.2
PID:20.500.12556/DKUM-97379 New window
UDC:621.39
ISSN on article:2820-364X
COBISS.SI-ID:17778966 New window
DOI:10.18690/analipazu.3.2.69-74.2013 New window
Publication date in DKUM:03.03.2026
Views:154
Downloads:1
Metadata:XML DC-XML DC-RDF
Categories:Misc.
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Record is a part of a journal

Title:Anali PAZU
Publisher:Združenje Pomurska akademsko znanstvena unija, Združenje Pomurska akademsko znanstvena unija, Univerzitetna založba Univerze v Mariboru
ISSN:2232-416X
COBISS.SI-ID:257553152 New window

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.

Secondary language

Language:Slovenian
Title:Unsupervised Training for Acoustic Models of Speech
Abstract:This paper presents unsupervised acoustical model training for automatic speech recognition. The main advantage of this training method is its speed and cost effectiveness compared to the manual transcription of speech, which is needed for supervised training. We present two methods of unsupervised training and test them on a large vocabulary continuous speech recognition system in the Broadcast News domain.
Keywords:akustični modeli, razpoznavanje govora, nenadzorovano učenje


Collection

This document is a part of these collections:
  1. Anali PAZU

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