Arabic speech recognition using recurrent neural networks

Choubassi, M.M. El; Khoury, Hilda; Alagha, C.E.J.; Skaf, Joëlle; Al-Alaoui, Mohamad Adnan

doi:10.1109/isspit.2003.1341178

Cited by 29 publications

(31 citation statements)

References 4 publications

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“…The next system [7] , which is a medium sized vocabulary system gave results that ranged from %8 and %4.2 WER. Small vocabulary isolated ASR systems like [9] obtained word error rate that ranged between %15 and %0, while [8] had a % 2.14 WER. Systems that concentrated on the Egyptian colloquial Arabic were all large vocabulary systems.…”

Section: Resultsmentioning

confidence: 99%

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CASRA+: A Colloquial Arabic Speech Recognition Application

Haraty¹,

Ariss²

2007

American J. of Applied Sciences

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The research proposed here was for an Arabic speech recognition application, concentrating on the Lebanese dialect. The system starts by sampling the speech, which was the process of transforming the sound from analog to digital and then extracts the features by using the Mel-Frequency Cepstral Coefficients (MFCC). The extracted features are then compared with the system's stored model; in this case the stored model chosen was a phoneme-based model. This reference model differs from the direct word template matching, where speech features that are extracted from the input are directly compared to the word templates. Each word template in the direct matching model was stored as a vector of feature parameters. Thus, when the vocabulary size of the ASR system becomes large, the memory size for the word template will become humongous. In contrast, the model used here was phoneme-like template matching. Word templates are stored as phoneme-like template parameters. Thus, the memory size for the word templates will not grow as fast as that of the direct matching model.

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Section: Resultsmentioning

confidence: 99%

“…The third work, an isolated speech recognition system based on Neural Network was done at the American University of Beirut [8] . Finally, another Neural Network system that also accepts isolated speech was developed by [9] . The second group focused on the recognition of Egyptian Colloquial Arabic (ECA).…”

Section: Variability Caused By Dialectical Differencesmentioning

confidence: 99%

CASRA+: A Colloquial Arabic Speech Recognition Application

Haraty¹,

Ariss²

2007

American J. of Applied Sciences

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“…Elman neural network is a special kind of a recurrent srly-network originally developed for speech recognition es), [1,4,10]. It is a two-layer network in which the hidden r of layer is recurrent.…”

Section: Elman Nn ) Tomentioning

confidence: 99%

Phoneme Recognition as a Member of Predefined Class using Hybrid Cascaded LVQ/Elman Neural Network

Avdagić

Nuhic

Konjicija

2007

2007 IEEE International Conference on Signal Processing and Communications

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What is presented is a new approach for implementing Bosnian phoneme recognition. While most of the literature on phoneme recognition is based on hidden Markov models (HMM), or on the recognition by neural networks (NN) of one type, the present system is implemented by hybrid cascaded LVQ/Elman NN. This model was created, because we noted that some types of NN achieve better recognition rate for some phonemes, while the other types of NN better recognize other phonemes. Presented system uses LVQ NN as a front-end recognizer, and depending on the obtained output, makes the re-recognition of the same phoneme by Elman NN. This system achieved higher recognition accuracy then standalone NN models.Index Terms-Bosnian phoneme recognition, Melfrequency cepstral coefficients, hybrid cascaded LVQ/Elman neural network.

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“…River water quality classifications have been conducted in [15] using SOM, Cluster Analysis and Principal Component Analysis and the results show that SOM is better than the other two methods. RNN has been used in [16] for Arabic speech recognition while in [17] RNN has been used to detect the real extent of snow in mountainous regions.…”

Section: Introductionmentioning

confidence: 99%

Document zone content classification for technical document images using Artificial Neural Networks and Support Vector Machines

İbrahim¹,

Isa

Rajkumar

et al. 2009

2009 Second International Conference on the Applications of Digital Information and Web Technologies

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Artificial Neural Networks (ANN) are a classic pattern classifier and widely applicable to various problems and are relatively easy to use. Three of the most popular ANNs are Multilayer Perceptron (MLP) with Backpropagation learning algorithm, Self Organizing Map (SOM) and Recurrent Neural Network (RNN). Support Vector Machines (SVM) have gained great interest in the last few years in pattern recognition. Thus, this research compares the recognition performance of text and non-text images (text, table, figure and graph) from technical document images based on the pixel intensity of various zones between BPNN, SOM, RNN and SVM. Symmetrical and non-symmetrical zoning algorithms were compared as input. 400 different datasets have been tested and the experiments indicate that SVM classification is superior to the other three classifiers. The experiments also indicate that the combination of symmetrical and non-symmetrical zoning design is better than non-symmetrical or symmetrical zoning only.

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Arabic speech recognition using recurrent neural networks

Cited by 29 publications

References 4 publications

CASRA+: A Colloquial Arabic Speech Recognition Application

CASRA+: A Colloquial Arabic Speech Recognition Application

Phoneme Recognition as a Member of Predefined Class using Hybrid Cascaded LVQ/Elman Neural Network

Document zone content classification for technical document images using Artificial Neural Networks and Support Vector Machines

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