@article {8688,
	title = {Bidirectional deep architecture for Arabic speech recognition},
	journal = {Open Computer Science (De Gruyter)},
	volume = {9},
	year = {2019},
	pages = {92{\textendash}102},
	abstract = {Nowadays, the real life constraints necessitatescontrolling modern machines using human interventionby means of sensorial organs. The voice is one of the humansenses that can control/monitor modern interfaces.In this context, Automatic Speech Recognition is principallyused to convert natural voice into computer text aswell as to perform an action based on the instructionsgiven by the human. In this paper, we propose a generalframework for Arabic speech recognition that uses LongShort-Term Memory (LSTM) and Neural Network (Multi-Layer Perceptron: MLP) classifier to cope with the nonuniformsequence length of the speech utterances issuedfromboth feature extraction techniques, (1)Mel FrequencyCepstral Coefficients MFCC (static and dynamic features),(2) the Filter Banks (FB) coefficients. The neural architecturecan recognize the isolated Arabic speech via classificationtechnique. The proposed system involves, first, extractingpertinent features from the natural speech signalusing MFCC (static and dynamic features) and FB. Next,the extracted features are padded in order to deal with thenon-uniformity of the sequences length. Then, a deep architecturerepresented by a recurrent LSTM or GRU (GatedRecurrent Unit) architectures are used to encode the sequencesofMFCC/FB features as a fixed size vector that willbe introduced to a Multi-Layer Perceptron network (MLP)to perform the classification (recognition). The proposedsystem is assessed using two different databases, the firstone concerns the spoken digit recognition where a comparisonwith other related works in the literature is performed,whereas the second one contains the spoken TVcommands. The obtained results show the superiority ofthe proposed approach.},
	url = {https://www.degruyter.com/view/j/comp.2019.9.issue-1/comp-2019-0004/comp-2019-0004.xml},
	author = {Naima Zerari and Samir Abdelhamid and Hassen Bouzgou and Christian Raymond}
}
