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DOI: 10.14569/IJACSA.2021.0120472
PDF

Analysis of Speech Signal Data of Mising Vowels using Logistic Regression and K-Means Clustering

Author 1: Ujjal Saikia
Author 2: Jiten Hazarika

International Journal of Advanced Computer Science and Applications(IJACSA), Volume 12 Issue 4, 2021.

  • Abstract and Keywords
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Abstract: In this paper, an attempt has been made to study and analyze speech signal data. Here, the sound or speech data has different attributes like time, pitch, formant frequencies, speaker type, Vowel No etc. The dataset used here is speech signal data which are analog in nature and has been converted to digital format. After converting the data into digital format we want to establish a Logit model to predict the speaker gender on the basis of the pitch signal values which is also considered as fundamental formant frequency. That is our objective is to predict whether a speaker is male or female by looking at the pitch value by using logistic regression. We have applied clustering techniques to visualize and interpret how it works in speech signal data. The logistic model gives us 91% accuracy rate with low and efficient AIC value where as in case of the clustering algorithm we get a 93% accuracy for the whole sample.

Keywords: Clustering methods; formant frequency; logit model; pitch; stype

Ujjal Saikia and Jiten Hazarika, “Analysis of Speech Signal Data of Mising Vowels using Logistic Regression and K-Means Clustering” International Journal of Advanced Computer Science and Applications(IJACSA), 12(4), 2021. http://dx.doi.org/10.14569/IJACSA.2021.0120472

@article{Saikia2021,
title = {Analysis of Speech Signal Data of Mising Vowels using Logistic Regression and K-Means Clustering},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2021.0120472},
url = {http://dx.doi.org/10.14569/IJACSA.2021.0120472},
year = {2021},
publisher = {The Science and Information Organization},
volume = {12},
number = {4},
author = {Ujjal Saikia and Jiten Hazarika}
}



Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

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