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The convolutional neural networks for Amazigh speech recognition system

Meryam Telmem, Youssef Ghanou
TELKOMNIKA (Telecommunication Computing Electronics and Control) , Volume 19, pp 515-522; doi:10.12928/telkomnika.v19i2.16793

Abstract: In this paper, we present an approach based on convolutional neural networks to build an automatic speech recognition system for the Amazigh language. This system is built with TensorFlow and uses mel frequency cepstral coefficient (MFCC) to extract features. In order to test the effect of the speaker's gender and age on the accuracy of the model, the system was trained and tested on several datasets. The first experiment the dataset consists of 9240 audio files. The second experiment the dataset consists of 9240 audio files distributed between females and males’ speakers. The last experiment 3 the dataset consists of 13860 audio files distributed between age 9-15, age 16-30, and age 30+. The result shows that the model trained on a dataset of adult speaker’s age +30 categories generates the best accuracy with 93.9%.
Keywords: neural / files / model / speech / adult / Audio / build / convolutional / speakers / Amazigh

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