Overview of Methods for Data Augmentation for Speech-to-Text and Text-to-Speech
Date Issued
2023-07
Author(s)
Penkova, Blagica
Mitreska, Maja
Simjanoska, Monika
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.
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.
Subjects
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