Journal of Intelligent Computing Irum Sindhu, Mohd Shamrie, Sanin 16 3 2025 https://doi.org/10.6025/jic/2025/16/3/114-124 https://www.dline.info/jic/fulltext/v16n3/jicv16n3_3.pdf Dysphonia, a voice disorder characterized by abnormal vocal quality, significantly impacts communication abilities. Accurate and early detection is crucial for effective treatment and intervention. This study compares the efficacy of various Recurrent Neural Network (RNN) variants in classifying dysphonia using the Uncommon Voice dataset and provides an evaluation of standard RNN, Gated Recurrent Unit (GRUs) and Long Short-Term Memory (LSTM) models. Each variant was trained and tested on the preprocessed dataset, split into 80:20 ratio of training and testing sets. The finding shows variations in model performance, where the standard RNN achieved an accuracy of 76%, while the LSTM and GRU models demonstrated superior accuraci -es of 94% and 93%, respectively. These results underscore the potential of advanced RNN variants, partic ularly LSTM and GRU, for dysphonia detection and classification. The analysis offers preliminary information n about the relative advantages and disadvantages of each RNN variant, paving the way for future resarch in the broader domain of speech sound disorder identification.