Speech quality based on Arabic pronunciation using MFCC and LDA: Investigating the emphatic consonants

Zainon, Nooraizedfiza; Ahmad, Z. A.; Romli, M. A.; Yaacob, Sazali

doi:10.1109/iccsce.2012.6487178

Cited by 3 publications

(1 citation statement)

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“…The experiment results showed that the proposed approach significantly outperformed previous approaches. Some researchers used LDA (Linear Discriminant Analysis) to classify the data into the correct class and draw a decision region between the given classes [22]. The first step in this system is preprocessing speech signals, which helps prepare the data for the next processing.…”

Section: Audio Featuresmentioning

confidence: 99%

An Approach for Pronunciation Classification of Classical Arabic Phonemes Using Deep Learning

et al. 2021

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A mispronunciation of Arabic short vowels can change the meaning of a complete sentence. For this reason, both the students and teachers of Classical Arabic (CA) are required extra practice for correcting students’ pronunciation of Arabic short vowels. That makes the teaching and learning task cumbersome for both parties. An intelligent process of students’ evaluation can make learning and teaching easier for both students and teachers. Given that online learning has become a norm these days, modern learning requires assessment by virtual teachers. In our case, the task is about recognizing the exact pronunciation of Arabic alphabets according to the standards. A major challenge in the recognition of precise pronunciation of Arabic alphabets is the correct identification of a large number of short vowels, which cannot be dealt with using traditional statistical audio processing techniques and machine learning models. Therefore, we developed a model that classifies Arabic short vowels using Deep Neural Networks (DNN). The model is constructed from scratch by: (i) collecting a new audio dataset, (ii) developing a neural network architecture, and (iii) optimizing and fine-tuning the developed model through several iterations to achieve high classification accuracy. Given a set of unseen audio samples of uttered short vowels, our proposed model has reached the testing accuracy of 95.77%. We can say that our results can be used by the experts and researchers for building better intelligent learning support systems in Arabic speech processing.

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Section: Audio Featuresmentioning

confidence: 99%