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Speaker Identification using Spectrograms of Varying Frame Sizes

by H. B. Kekre, Vaishali Kulkarni, Prashant Gaikar, Nishant Gupta
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 50 - Number 20
Year of Publication: 2012
Authors: H. B. Kekre, Vaishali Kulkarni, Prashant Gaikar, Nishant Gupta
10.5120/7921-1228

H. B. Kekre, Vaishali Kulkarni, Prashant Gaikar, Nishant Gupta . Speaker Identification using Spectrograms of Varying Frame Sizes. International Journal of Computer Applications. 50, 20 ( July 2012), 27-33. DOI=10.5120/7921-1228

@article{ 10.5120/7921-1228,
author = { H. B. Kekre, Vaishali Kulkarni, Prashant Gaikar, Nishant Gupta },
title = { Speaker Identification using Spectrograms of Varying Frame Sizes },
journal = { International Journal of Computer Applications },
issue_date = { July 2012 },
volume = { 50 },
number = { 20 },
month = { July },
year = { 2012 },
issn = { 0975-8887 },
pages = { 27-33 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume50/number20/7921-1228/ },
doi = { 10.5120/7921-1228 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:48:50.502156+05:30
%A H. B. Kekre
%A Vaishali Kulkarni
%A Prashant Gaikar
%A Nishant Gupta
%T Speaker Identification using Spectrograms of Varying Frame Sizes
%J International Journal of Computer Applications
%@ 0975-8887
%V 50
%N 20
%P 27-33
%D 2012
%I Foundation of Computer Science (FCS), NY, USA
Abstract

In this paper, a text dependent speaker recognition algorithm based on spectrogram is proposed. The spectrograms have been generated using Discrete Fourier Transform for varying frame sizes with 25% and 50% overlap between speech frames. Feature vector extraction has been done by using the row mean vector of the spectrograms. For feature matching, two distance measures, namely Euclidean distance and Manhattan distance have been used. The results have been computed using two databases: a locally created database and CSLU speaker recognition database. The maximum accuracy is 92. 52% for an overlap of 50% between speech frames with Manhattan distance as similarity measure.

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Index Terms

Computer Science
Information Sciences

Keywords

Discrete Fourier Transform (DFT) Row Mean Euclidean Distance Manhattan distance

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