This study investigates the efficacy of various machine learning algorithms (MLAs) in identifying tonal contrasts within the endangered Chokri language. Seven supervised MLAs (Logistic Regression, Decision Tree, Random Forest, Support Vector Machine, K-Nearest Neighbors, Naive Bayes) and an Artificial Neural Network (ANN) were employed to analyze five-way tonal contrasts. Acoustic correlates, focusing on fundamental frequency (f0) height and direction, were examined. Random Forest demonstrated superior accuracy, exceeding the performance of ANN. The study also revealed that combining f0 height and direction improved recognition for female speakers, while f0 direction alone sufficed for males. Accuracy rates reached 84–87% for females and 95–97% for males, highlighting the potential of MLAs in analyzing tonal languages and suggesting broader applications across various fields.
