Classifying Oromo fricatives with machine learning

Using machine learning, this study investigates how segment duration, spectral moments, and DCT coefficients help differentiating singleton from geminate in Oromo fricatives. Eighteen native Oromo speakers from the western dialect produced speech data consisting of the fricatives in an intervocalic position. Acoustic features, including segment duration, bark-transformed spectral moments, and the first six DCT coefficients, were extracted from the midpoint of the speech data. Support Vector Machine, Random Forest, and Multilayer Perceptron Neural Network classified the sounds. Results reveal that segment duration is the most consistent feature for distinguishing singleton and geminate, with DCT coefficients slightly outperforming spectral moments. The highest classification accuracy could be achieved by combining duration and spectral moments, but non-temporal features result in more errors.