Automated Pipeline for Training Dataset Creation from Unlabeled Audios for Automatic Speech Recognition

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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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
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: Статті у наукометричних базах > Scopus
Divisions: Факультети > Факультет інформаційних технологій та управління > Кафедра інформаційної та кібернетичної безпеки
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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