@proceedings {64782,
	title = {Bi-directional Recurrent End-to-End Neural Network Classifier for Spoken Arab Digit Recognition},
	journal = {2nd International Conference on Natural Language and Speech Processing (ICNLSP)},
	year = {2018},
	month = {2018},
	address = {Alger, Algeria},
	abstract = {Automatic Speech Recognition can be considered as a transcription of spoken utterances into text which can be used to monitor/command a specific system. In this paper, we propose a general end-to-end approach to sequence learning that uses Long Short-Term Memory (LSTM) to deal with the non-uniform sequence length of the speech utterances. The neural architecture can recognize the Arabic spoken digit spelling of an isolated Arabic word using a classification methodology, with the aim to enable natural human-machine interaction. The proposed system consists to, first, extract the relevant features from the input speech signal using Mel Frequency Cepstral Coefficients (MFCC) and then these features are processed by a deep neural network able to deal with the non uniformity of the sequences length. A recurrent LSTM or GRU architecture is used to encode sequences of MFCC features as a fixed size vector that will feed a multilayer perceptron network to perform the classification. The whole neural network classifier is trained in an end-to-end manner. The proposed system outperforms by a large gap the previous published results on the same database.},
	url = {https://ieeexplore.ieee.org/abstract/document/8374374},
	author = {Naima, Zerari and Samir Abdelhamid and Hassen Bouzgou and Christian Raymond}
}
