Romanovskyi, O. and Iosifova, Olena and Iosifov, Ievgen and Sokolov, V. Y. and Kipchuk, F. and Sukaylo, I. (2021) Automated Pipeline for Training Dataset Creation from Unlabeled Audios for Automatic Speech Recognition Lecture Notes on Data Engineering and Communications Technologies, 83. pp. 25-36. ISSN 2194-5365
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Romanovskyi_O_Iosifov_I_Iosifova_O_Sokolov_V_Kipchuk_F_Sukaylo_I_DECT_83.pdf - Published Version Download (88kB) |
Abstract
In the paper, we present a software pipeline for speech recognition to automate the creation of training datasets, based on desired unlabeled audios, for low resource languages and domain-specific area. Considering the commoditizing of speech recognition, more teams build domain-specific models as well as models for local languages. At the same time, lack of training datasets for low to middle resource languages significantly decreases possibilities to exploit last achievements and frameworks in the Speech Recognition area and limits the wide range of software engineers to work on speech recognition problems. This problem is even more critical for domain-specific datasets. The pipeline was tested for building Ukrainian language recognition and confirmed that the created design is adaptable to different data source formats and expandable to integrate with existing frameworks.
Item Type: | Article |
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Additional Information: | EID: 2-s2.0-85111941280 DOI: 10.1007/978-3-030-80472-5_3 |
Uncontrolled Keywords: | Automatic Speech Recognition; ASR; Dataset creation pipeline; Natural language processing; NLP; Asynchronous graphs |
Subjects: | Це архівна тематика Київського університету імені Бориса Грінченка > Статті у наукометричних базах > Scopus |
Divisions: | Це архівні підрозділи Київського університету імені Бориса Грінченка > Факультет інформаційних технологій та математики > Кафедра інформаційної та кібернетичної безпеки імені професора Володимира Бурячка |
Depositing User: | Volodymyr Sokolov |
Date Deposited: | 17 Aug 2021 11:32 |
Last Modified: | 17 Aug 2021 12:05 |
URI: | https://elibrary.kubg.edu.ua/id/eprint/36974 |
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