Non-diacritized Arabic speech recognition based on CNN-LSTM and attention-based models

Alsayadi, Hamzah, Abdelhamid, Abdelaziz, Hegazy, Islam, Fayed, Zaki T,

Abstract


Arabic language has a set of sound letters called diacritics, these diacritics play an essential role in the meaning of words and their articulations. The change in some diacritics leads to a change in the context of the sentence. However, the existence of these letters in the corpus transcription affects the accuracy of speech recognition. In this paper, we investigate the effect of diactrics on the Arabic speech recognition based end-to-end deep learning. The applied end-to-end approach includes CNN-LSTM and attention-based technique presented in the state-of-the-art framework namely, Espresso using Pytorch. In addition, and to the best of our knowledge, the approach of CNN-LSTM with attention-based has not been used in the task of Arabic Automatic speech recognition (ASR). To fill this gap, this paper proposes a new approach based on CNN-LSTM with attention based method for Arabic ASR. The language model in this approach is trained using RNN-LM and LSTM-LM and based on nondiacritized transcription of the speech corpus. The Standard Arabic Single Speaker Corpus (SASSC), after omitting the diacritics, is used to train and test the deep learning model. Experimental results show that the removal of diacritics decreased out-of-vocabulary and perplexity of the language model. In addition, the word error rate (WER) is significantly improved when compared to diacritized data. The achieved average reduction in WER is 13.52%.


Other data

Title Non-diacritized Arabic speech recognition based on CNN-LSTM and attention-based models
Authors Alsayadi, Hamzah ; Abdelhamid, Abdelaziz ; Hegazy, Islam ; Fayed, Zaki T 
Keywords Arabic speech recognition;Arabic diacritics;End-to-End deep learning;CNN-LSTM
Issue Date 16-Dec-2021
Publisher IOS press
Journal Journal of Intelligent & Fuzzy Systems 
Volume 41
Issue 6
Start page 6207
End page 6219
ISSN 10641246
18758967
DOI 10.3233/JIFS-202841
Scopus ID 2-s2.0-85121998095

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