Nadia Hmad

Echo State Networks For Arabic Phoneme Recognition

This paper presents an ESN-based Arabic phoneme recognition system trained with supervi... more This paper presents an ESN-based Arabic phoneme recognition system trained with supervised, forced and combined supervised/forced supervised learning algorithms. Mel-Frequency Cepstrum Coefficients (MFCCs) and Linear Predictive Code (LPC) techniques are used and compared as the input feature extraction technique. The system is evaluated using 6 speakers from the King Abdulaziz Arabic Phonetics Database (KAPD) for Saudi Arabia dialectic and 34 speakers from the Center for Spoken Language Understanding (CSLU2002) database of speakers with different dialectics from 12 Arabic countries. Results for the KAPD and CSLU2002 Arabic databases show phoneme recognition performances of 72.31% and 38.20% respectively.

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Echo State Networks for Arabic Phoneme Recognition

This paper presents an ESN-based Arabic phoneme recognition system trained with supervised, force... more This paper presents an ESN-based Arabic phoneme recognition system trained with supervised, forced and combined supervised/forced supervised learning algorithms. Mel-Frequency Cepstrum Coefficients (MFCCs) and Linear Predictive Code (LPC) techniques are used and compared as the input feature extraction technique. The system is evaluated using 6 speakers from the King Abdulaziz Arabic Phonetics Database (KAPD) for Saudi Arabia dialectic and 34 speakers from the Center for Spoken Language Understanding (CSLU2002) database of speakers with different dialectics from 12 Arabic countries. Results for the KAPD and CSLU2002 Arabic databases show phoneme recognition performances of 72.31% and 38.20% respectively. Keywords—Arabic phonemes recognition, echo state networks (ESNs), neural networks (NNs), supervised learning.

Deep neural network acoustic models for multi-dialect Arabic speech recognition

Speech is a desirable communication method between humans and computers. The major concerns of th... more Speech is a desirable communication method between humans and computers. The major concerns of the automatic speech recognition (ASR) are determining a set of classification features and finding a suitable recognition model for these features. Hidden Markov Models (HMMs) have been demonstrated to be powerful models for representing time varying signals. Artificial Neural Networks (ANNs) have also been widely used for representing time varying quasi-stationary signals. Arabic is one of the oldest living languages and one of the oldest Semitic languages in the world, it is also the fifth most generally used language and is the mother tongue for roughly 200 million people. Arabic speech recognition has been a fertile area of reasearch over the previous two decades, as attested by the various papers that have been published on this subject. This thesis investigates phoneme and acoustic models based on Deep Neural Networks (DNN) and Deep Echo State Networks for multi-dialect Arabic Speec...

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Echo State Networks for Arabic Phoneme Recognition

World Academy of Science, Engineering and Technology, International Journal of Computer, Electrical, Automation, Control and Information Engineering, 2013

This paper presents an ESN-based Arabic phoneme recognition system trained with supervised, force... more This paper presents an ESN-based Arabic phoneme recognition system trained with supervised, forced and combined supervised/forced supervised learning algorithms. Mel-Frequency Cepstrum Coefficients (MFCCs) and Linear Predictive Code (LPC) techniques are used and compared as the input feature extraction technique. The system is evaluated using 6 speakers from the King Abdulaziz Arabic Phonetics Database (KAPD) for Saudi Arabia dialectic and 34 speakers from the Center for Spoken Language Understanding (CSLU2002) database of speakers with different dialectics from 12 Arabic countries. Results for the KAPD and CSLU2002 Arabic databases show phoneme recognition performances of 72.31% and 38.20% respectively. Keywords—Arabic phonemes recognition, echo state networks (ESNs), neural networks (NNs), supervised learning.

Biologically inspired Continuous Arabic Speech Recognition

Despite many years of research into speech recognition systems, there are limited research public... more Despite many years of research into speech recognition systems, there are limited research publications available covering Arabic speech recognition. Although statistical techniques have been the most applied techniques for such classification problems, Neural Networks have also recorded successful results in speech recognition. In this research three different biologically inspired Continuous Arabic Speech Recognition neural network system structures are presented. An Arabic phoneme database (APD) of six male speakers was constructed manually from the King Abdulaziz Arabic Phonetics Database (KAPD). The Mel-Frequency Cepstrum Coefficients (MFCCs) algorithm was used to extract the phoneme features from the speech signals of this database. The normalized dataset was used to train and test three different architectures of Multilayer Perceptron (MLP) neural network identification systems.

Removing the Impulse Noise from Grayscaled and Colored Digital Images Using Fuzzy Image Filtering

2021 IEEE 1st International Maghreb Meeting of the Conference on Sciences and Techniques of Automatic Control and Computer Engineering MI-STA

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