Please use this identifier to cite or link to this item:
https://hdl.handle.net/20.500.11779/1568
Title: | Domain adaptation approaches for acoustic modeling | Other Titles: | Akustik modelleme için alana uyarlama yaklaşımları | Authors: | Fakhan, Enver Arısoy, Ebru |
Keywords: | Adaptation models Computational modeling Neural networks Data models Art Transforms Training data Akustik model uyarlama Otomatik konuşma tanıma Yapay sinir ağları |
Publisher: | IEEE | Source: | E. Fakhan and E. Arısoy, (5-7 Oct. 2020). Domain Adaptation Approaches for Acoustic Modeling," 2020 28th Signal Processing and Communications Applications Conference (SIU), pp. 1-4, doi: 10.1109/SIU49456.2020.9302343. | Abstract: | In the recent years, with the development of neural network based models, ASR systems have achieved a tremendous performance increase. However, this performance increase mostly depends on the amount of training data and the computational power. In a low-resource data scenario, publicly available datasets can be utilized to overcome data scarcity. Furthermore, using a pre-trained model and adapting it to the in-domain data can help with computational constraint. In this paper we have leveraged two different publicly available datasets and investigate various acoustic model adaptation approaches. We show that 4% word error rate can be achieved using a very limited in-domain data. | URI: | https://hdl.handle.net/20.500.11779/1568 https://doi.org/10.1109/SIU49456.2020.9302343 |
ISBN: | 9781728172064 | ISSN: | 2165-0608 |
Appears in Collections: | Elektrik Elektronik Mühendisliği Bölümü koleksiyonu Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
Domain_Adaptation_Approaches_for_Acoustic_Modeling.pdf Until 2040-01-01 | Proceedings Paper | 503.49 kB | Adobe PDF | View/Open Request a copy |
CORE Recommender
SCOPUSTM
Citations
1
checked on Aug 1, 2024
WEB OF SCIENCETM
Citations
1
checked on Jun 23, 2024
Page view(s)
4
checked on Jun 26, 2024
Google ScholarTM
Check
Altmetric
Items in GCRIS Repository are protected by copyright, with all rights reserved, unless otherwise indicated.