Ве молиме користете го овој идентификатор да го цитирате или поврзете овој запис: http://hdl.handle.net/20.500.12188/27404
Наслов: Overview of Methods for Data Augmentation for Speech-to-Text and Text-to-Speech
Authors: Penkova, Blagica
Mitreska, Maja
Mishev, Kostadin 
Simjanoska, Monika
Keywords: Data augmentation, Speech-to-text, Text-tospeech
Issue Date: јул-2023
Publisher: Ss Cyril and Methodius University in Skopje, Faculty of Computer Science and Engineering, Republic of North Macedonia
Series/Report no.: CIIT 2023 papers;29;
Conference: 20th International Conference on Informatics and Information Technologies - CIIT 2023
Abstract: In the field of machine learning and deep learning, data augmentation is a widely used technique to expand the amount of training data available. This involves altering existing data instances or generating new synthetic data, with the aim of enhancing the quantity and variability of the training set.It has shown to be especially useful when working with low resource languages and domains, where datasets are limited. This paper provides an overview of the data augmentation methods used for speech-related tasks, specifically for speech to-text and text-to-speech applications.The goal of this paper is to provide researchers and practitioners with a comprehensive understanding of the data augmentation methods available for speech-related tasks, their strengths and potential applications.
URI: http://hdl.handle.net/20.500.12188/27404
Appears in Collections:Faculty of Computer Science and Engineering: Conference papers

Files in This Item:
File Опис SizeFormat 
CIIT2023_paper_29.pdf9.19 MBAdobe PDFView/Open
Прикажи целосна запис

Page view(s)

147
checked on 4.5.2025

Download(s)

170
checked on 4.5.2025

Google ScholarTM

Проверете


Записите во DSpace се заштитени со авторски права, со сите права задржани, освен ако не е поинаку наведено.