Artificial Neural Network Based Amharic Language Speaker Recognition

In this artificial intelligence time, speaker recognition is the most useful biometric recognition technique. Security is a big issue that needs careful attention because of every activities have been becoming automated and internet based. For security purpose, unique features of authorized user are...

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Bibliographic Details
Published in:Turkish journal of computer and mathematics education 2021-04, Vol.12 (3), p.5105-5116
Main Authors: Gebre, Gizachew Belayneh, Urgessa, Teklu, Gopikrishna, T
Format: Article
Language:English
Subjects:
Artificial intelligence
Artificial neural networks
Biometrics
Crime
Feature extraction
Feature recognition
High level languages
Neural networks
Probabilistic models
Programming languages
Scientific Research
Security
Semitic Languages
Speech
Speech recognition
Vector quantization
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Summary:In this artificial intelligence time, speaker recognition is the most useful biometric recognition technique. Security is a big issue that needs careful attention because of every activities have been becoming automated and internet based. For security purpose, unique features of authorized user are highly needed. Voice is one of the wonderful unique biometric features. So, developing speaker recognition based on scientific research is the most concerned issue. Nowadays, criminal activities are increasing day to day in different clever way. So, every country should have strengthen forensic investigation using such technologies. The study was done by inspiration of contextualizing this concept for our country. In this study, text-independent Amharic language speaker recognition model was developed using Mel-Frequency Cepstral Coefficients to extract features from preprocessed speech signals and Artificial Neural Network to model the feature vector obtained from the Mel-Frequency Cepstral Coefficients and to classify objects while testing. The researcher used 20 sampled speeches of 10 each speaker (total of 200 speech samples) for training and testing separately. By setting the number of hidden neurons to 15, 20, and 25, three different models have been developed and evaluated for accuracy. The fourth-generation high-level programming language and interactive environment MATLAB is used to conduct the overall study implementations. At the end, very promising findings have been obtained. The study achieved better performance than other related researches which used Vector Quantization and Gaussian Mixture Model modelling techniques. Implementable result could obtain for the future by increasing number of speakers and speech samples and including the four Amharic accents.
ISSN:1309-4653
1309-4653
DOI:10.17762/turcomat.v12i3.2043