It delivers robust performance with an average accuracy rate of 81.52%, representing a significant improvement over previous mispronunciation detection systems.

Jul 13, 2023 · Our results show that the LSTM network significantly enhances mispronunciation detection along with gender recognition. The LSTM models attained ...

Arabic Mispronunciation Recognition System Using LSTM Network. Language ... LSTM network significantly enhances mispronunciation detection along with gender ...

Much recent research dealt with Arabic mispronunciation detection using deep neural networks (DNN). To identify Arabic articulation problems for learners.

Nov 18, 2024 · The results show that the ResNet18 classifier on speech-to-image converted data effectively identifies mispro- nunciations in Arabic speech with ...

6 days ago · This paper introduces an Arabic MDD system using transformer-based techniques for non-native learners of spoken Arabic language to enhance their ...

Jun 25, 2024 · The obtained results are promising, with an accuracy of about 98%. The proposed VAE outperformed the standard autoencoder as well as the state- ...

المؤلفون المشاركون ; Arabic Mispronunciation Recognition System Using LSTM Network‏. A Ahmed, M Bader, I Shahin, AB Nassif, N Werghi, M Basel‏. Information 14 (7) ...

This paper introduces a novel Arabic pronunciation learning application QVoice, powered with end-to-end mispronunciation detection and feedback generator module ...

To overcome these problems, we proposed a solution that consists of Mel-Frequency Cepstral Coefficient (MFCC) features with Long Short-Term Memory (LSTM) neural.