Proceedings of the International Conference on Educational Management and Technology (ICEMT 2022)

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Comparison Euclidean Distance and Manhattan Distance as Classification in Speech Recognition System

Authors
Muhammad Ryandy Ghonim Asgar1, *, Risanuri Hidayat1, Agus Bejo1
1Gadjah Mada University Yogyakarta, Yogyakarta, Indonesia
*Corresponding author. Email: muhammadryandy@mail.ugm.ac.id
Corresponding Author
Muhammad Ryandy Ghonim Asgar
Available Online 13 February 2023.
DOI
10.2991/978-2-494069-95-4_54How to use a DOI?
Keywords
Speech Recognition; MFCC; Euclidean Distance; Manhattan Distance; K-Fold Cross Validation
Abstract

One of the uses of a digital system is a speech recognition system. Feature extraction and classification is important step in speech recognition system process. Mel Frequency Cepstrum Coefficient (MFCC) feature extraction is a popular feature extraction used in speech recognition system, while one of the most popular classification technique is K Nearest Neighbour (KNN). There are many KNN classification techniques, but the most commonly used are the Euclidean Distance and Manhattan Distance. Research on speech recognition system in Indonesia and in particular the Indonesian speech recognition system is still very limited, far from the recognition system in English. Therefore, this paper proposes a comparison of the best accuracy generated by the classification between Euclidean distance and Manhattan distance using MFCC as a feature extraction in Indonesian speech recognition system. The model and testing of the proposed system used is 120 data, with 0 to 9 voice signals in Indonesian. By using the 13 coefficients from the MFCC and using 5-fold cross validation to achieve generalized results, the Euclidean distance is able to outperform the accuracy obtained by the Manhattan distance by a value of 88%.

Copyright
© 2023 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title
Proceedings of the International Conference on Educational Management and Technology (ICEMT 2022)
Series
Advances in Social Science, Education and Humanities Research
Publication Date
13 February 2023
ISBN
978-2-494069-95-4
ISSN
2352-5398
DOI
10.2991/978-2-494069-95-4_54How to use a DOI?
Copyright
© 2023 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - Muhammad Ryandy Ghonim Asgar
AU  - Risanuri Hidayat
AU  - Agus Bejo
PY  - 2023
DA  - 2023/02/13
TI  - Comparison Euclidean Distance and Manhattan Distance as Classification in Speech Recognition System
BT  - Proceedings of the International Conference on Educational Management and Technology (ICEMT 2022)
PB  - Atlantis Press
SP  - 454
EP  - 463
SN  - 2352-5398
UR  - https://doi.org/10.2991/978-2-494069-95-4_54
DO  - 10.2991/978-2-494069-95-4_54
ID  - Asgar2023
ER  -