Speech Processing for Text Independent Amharic Language Dialect Recognition
Dialect is a difference of verbal communication spoken by people from a particular society or geographic area so the paper focuses on Amharic language dialect recognition. In this paper, the authors have used backpropagation artificial neural network, VQ(vector quantization), (Gaussian Mixture Mode...
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| Format: | EJournal Article |
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Institute of Advanced Engineering and Science,
2017-01-01.
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| LEADER | 02607 am a22003253u 4500 | ||
|---|---|---|---|
| 001 | 0 nhttps:__ijeecs.iaescore.com_index.php_IJEECS_article_downloadSuppFile_6055_281 | ||
| 042 | |a dc | ||
| 100 | 1 | 0 | |a Mengistu, Abrham Debasu |e author |
| 100 | 1 | 0 | |e contributor |
| 700 | 1 | 0 | |a Alemayehu, Dagnachew Melesew |e author |
| 245 | 0 | 0 | |a Speech Processing for Text Independent Amharic Language Dialect Recognition |
| 260 | |b Institute of Advanced Engineering and Science, |c 2017-01-01. | ||
| 500 | |a https://ijeecs.iaescore.com/index.php/IJEECS/article/view/6055 | ||
| 520 | |a Dialect is a difference of verbal communication spoken by people from a particular society or geographic area so the paper focuses on Amharic language dialect recognition. In this paper, the authors have used backpropagation artificial neural network, VQ(vector quantization), (Gaussian Mixture Models) and a combination of GMM and backpropagation artificial neural network for classifying dialects of Amharic language speakers. In this research, a total of 100 speakers for each group of dialects are considered each having about 10 seconds duration is collected. The feature vectors of Mel frequency cepstral coefficients (MFCC) had been used to recognize the dialects of speakers. In this research paper the recognition model that uses a tanh activation function have a better result instead of using the Logistic Sigmoid activation function in backpropagation artificial neural network. After conducting the above experiments 95.7% accuracy achieved when GMM and backpropagation artificial neural network with tanh activation function are combined. | ||
| 540 | |a Copyright (c) 2017 Indonesian Journal of Electrical Engineering and Computer Science | ||
| 540 | |a http://creativecommons.org/licenses/by-nc-nd/4.0 | ||
| 546 | |a eng | ||
| 690 | |a Computer Science | ||
| 690 | |a Amharic language, Speaker recognition, MFCC, GFCC | ||
| 655 | 7 | |a info:eu-repo/semantics/article |2 local | |
| 655 | 7 | |a info:eu-repo/semantics/publishedVersion |2 local | |
| 655 | 7 | |2 local | |
| 786 | 0 | |n Indonesian Journal of Electrical Engineering and Computer Science; Vol 5, No 1: January 2017; 115-122 | |
| 786 | 0 | |n 2502-4760 | |
| 786 | 0 | |n 2502-4752 | |
| 786 | 0 | |n 10.11591/ijeecs.v5.i1 | |
| 787 | 0 | |n https://ijeecs.iaescore.com/index.php/IJEECS/article/view/6055/5745 | |
| 787 | 0 | |n https://ijeecs.iaescore.com/index.php/IJEECS/article/downloadSuppFile/6055/281 | |
| 856 | 4 | 1 | |u https://ijeecs.iaescore.com/index.php/IJEECS/article/view/6055/5745 |z Get fulltext |
| 856 | 4 | 1 | |u https://ijeecs.iaescore.com/index.php/IJEECS/article/downloadSuppFile/6055/281 |z Get fulltext |