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Exploring the Impact of Data Augmentation Techniques on Automatic Speech Recognition System Development: A Comparative Study

GALIC, J. See more information about GALIC, J. on SCOPUS See more information about GALIC, J. on IEEExplore See more information about GALIC, J. on Web of Science, GROZDIC, D. See more information about GROZDIC, D. on SCOPUS See more information about GROZDIC, D. on SCOPUS See more information about GROZDIC, D. on Web of Science
 
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Download PDF pdficon (1,307 KB) | Citation | Downloads: 914 | Views: 943

Author keywords
artificial neural networks, audio databases, automatic speech recognition, hidden markov models, support vector machines

References keywords
speech(22), recognition(15), data(13), augmentation(12), processing(7), audio(7), interspeech(6), signal(5), science(5), whispered(4)
Blue keywords are present in both the references section and the paper title.

About this article
Date of Publication: 2023-08-31
Volume 23, Issue 3, Year 2023, On page(s): 3 - 12
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2023.03001
Web of Science Accession Number: 001062641900001
SCOPUS ID: 85172345871

Abstract
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Automatic Speech Recognition (ASR) systems are notorious for their poor performance in adverse conditions, leading to high sensitivity and low robustness. Due to the costly and time-consuming nature of creating extensive speech databases, addressing the issue of low robustness has become a prominent area of research, focusing on the synthetic generation of speech data using pre-existing natural speech. This paper examines the impact of standard data augmentation techniques, including pitch shift, time stretch, volume control, and their combination, on the accuracy of isolated-word ASR systems. The performance of three machine learning models, namely Hidden Markov Models (HMM), Support Vector Machines (SVM), and Convolutional Neural Networks (CNN), is analyzed on two Serbian corpora of isolated words. The Whi-Spe speech database in neutral phonation is utilized for augmentation and training, and a specifically developed Python-based software tool is employed for the augmentation process in this research study. The conducted experiments demonstrate a statistically significant reduction in the Word Error Rate (WER) for the CNN-based recognizer on both testing datasets, achieved through a single augmentation technique based on pitch-shifting.


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References Weight

Web of Science® Citations for all references: 15,471 TCR
SCOPUS® Citations for all references: 18,163 TCR

Web of Science® Average Citations per reference: 397 ACR
SCOPUS® Average Citations per reference: 466 ACR

TCR = Total Citations for References / ACR = Average Citations per Reference

We introduced in 2010 - for the first time in scientific publishing, the term "References Weight", as a quantitative indication of the quality ... Read more

Citations for references updated on 2024-06-15 20:45 in 176 seconds.




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