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%