@article{4552,
  author = {Irum Sindhu, Mohd Shamrie, Sanin},
  title = {Evaluating RNN Variants for Dysphonia Classification using the Uncommon Voice Dataset: A Comparative Analysis},
  journal = {Journal of Intelligent Computing},
  year = {2025},
  volume = {16},
  number = {3},
  doi = {https://doi.org/10.6025/jic/2025/16/3/114-124},
  url = {https://www.dline.info/jic/fulltext/v16n3/jicv16n3_3.pdf},
  abstract = {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.},
}