Proceedings of the International Conference on Innovation in Science and Technology (ICIST 2020)

Accent Recognition Using Mel-Frequency Cepstral Coefficients and Convolutional Neural Network

Authors
Dwi Sari Widyowaty*, Andi Sunyoto, Hanif Al Fatta
Magister of Informatics Engineering, Universitas Amikom Yogyakarta, Yogyakarta, Indonesia
Corresponding Author
Dwi Sari Widyowaty
Available Online 30 November 2021.
DOI
10.2991/aer.k.211129.010How to use a DOI?
Keywords
accent recognition; speech recognition; MFCC; CNN
Abstract

Everyone has a different accent, the environment and culture can influence the difference in accents. Utilization of the recognition of the speaker’s accent can be used as a method to detect the speaker’s country of origin. Accent recognition belongs to the field of Automatic Speech Recognition (ASR), research on accent recognition is a step towards a smarter and more sophisticated ASR. Recently, ASR has become a trend in technology, such as virtual assistants. This study aimed to classify accents from several countries, namely English, Arabic, French, Spanish, and Mandarin, where all speakers used the same English Script. Previous research on accent recognition achieved 48.24 % using Mel Frequency Cepstral Coefficients (MFCC) and 2-layer Convolutional Neural Network (CNN). This study increases the accuracy by improving the pre-processing and the CNN model, the methods resulting 51.96 % accuracy using the similar of datasets as the previous study namely 1231 speakers and the methods namely Mel Frequency Cepstral Coefficients (MFCC) and 2-layer Convolutional Neural Network (CNN). By splitting the audio segment at the pre-processing and improving the model of CNN, it turns out to produce better accuracy.

Copyright
© 2021 The Authors. Published by Atlantis Press International B.V.
Open Access
This is an open access article under the CC BY-NC license.

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Volume Title
Proceedings of the International Conference on Innovation in Science and Technology (ICIST 2020)
Series
Advances in Engineering Research
Publication Date
30 November 2021
ISBN
10.2991/aer.k.211129.010
ISSN
2352-5401
DOI
10.2991/aer.k.211129.010How to use a DOI?
Copyright
© 2021 The Authors. Published by Atlantis Press International B.V.
Open Access
This is an open access article under the CC BY-NC license.

Cite this article

TY  - CONF
AU  - Dwi Sari Widyowaty
AU  - Andi Sunyoto
AU  - Hanif Al Fatta
PY  - 2021
DA  - 2021/11/30
TI  - Accent Recognition Using Mel-Frequency Cepstral Coefficients and Convolutional Neural Network
BT  - Proceedings of the International Conference on Innovation in Science and Technology (ICIST 2020)
PB  - Atlantis Press
SP  - 43
EP  - 46
SN  - 2352-5401
UR  - https://doi.org/10.2991/aer.k.211129.010
DO  - 10.2991/aer.k.211129.010
ID  - Widyowaty2021
ER  -