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Your search keyword '"Djeradi, Amar."' showing total 4 results
Start Over Author "Djeradi, Amar." Topic mfcc
4 results on '"Djeradi, Amar."'

1. Gender and Age Extraction from Audio Signal Using Convolutional Neural Network, MFCC and Spectrogram

Author

Karaoui, Fazia, Djeradi, Rachida, Djeradi, Amar, Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Daimi, Kevin, editor, and Al Sadoon, Abeer, editor

Published
2024
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3. Audiovisual Speaker Identification Based on Lip and Speech Modalities.

Author

Chelali, Fatma and Djeradi, Amar

Subjects
VOICEPRINTS, AUDIOVISUAL materials, DATA fusion (Statistics), DISCRETE cosine transforms, LINEAR predictive coding
Abstract

In this article, we present a bimodal speaker identification method, which integrates both acoustic and visual features and where the two audiovisual stream modalities are processed in parallel. We also propose a fusion technique that combines the two modalities to make the final recognition decision. Experiments are conducted on an audiovisual dataset containing the 28 Arabic syllables pronounced by ten speakers. Results show the importance of the visual information that is provided by Discrete Cosine Transform (DCT) and Discrete Wavelet Transform (DWT) in addition to the audio information corresponding to the Mel Frequency Cepstral Coefficients (MFCC) and Perceptual Linear Predictive (PLP). Furthermore, some artificial neural networks such as Multilayer Perceptron (MLP) and Radial Basis Function (RBF) were investigated and tested successfully in this dataset by presenting good recognition performances with serial concatenation for the acoustic and visual vectors. [ABSTRACT FROM AUTHOR]

Published
2017

4. MFCC and vector quantization for Arabic fricatives speech/speaker recognition.

Author

Chelali, Fatma Zohra. and Djeradi, Amar.

Abstract

This article develops a speaker-dependent Arabic phonemes recognition system using MFCC analysis and the VQ-LBG algorithm. The system is examined with and without vector quantization in order to analyze the effect of compression in an acoustic parameterization phase. Our experimental results show that vector quantization using a codebook of size 16 achieves good results compared to the system without quantization for a majority of the phonemes studied. [ABSTRACT FROM PUBLISHER]

Published
2012
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