Research Article
Deep Learning Methods for Arabic Autoencoder Speech Recognition System for Electro-Larynx Device
Table 4
Comparison of the proposed model with related works for Arabic language recognition.
| Reference | Model’s name | Accuracy | Word error rate |
| Jaber and Abdulbaqi [28] | Autoencoder (CNN) | 93% | — | Eljawad et al. [7] | Fuzzy neural network | 94.5% | — | Dendani et al. [11] | Autoencoder (MLP) | 65.72% | — | Alsayadi et al. [14] | Autoencoder (LSTM) | 71.58 | 28.42% | Alsayadi et al. [15] | CNN-LSTM | — | 13.52% | Proposed model | Autoencoder (GRU) | 95.31% | 4.69% |
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