The Improved Kurdish Dialect Classification Using Data Augmentation and ANOVA-Based Feature Selection

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Analyzing dialects in the Kurdish language proves to be tough because of the tiny phonetic distinctions among the dialects. We applied advanced methods to enhance the precision of Kurdish dialect classification in this research. We examined the dataset’s stability and variation through the use of time-stretching and noise-augmenting methods. Analysis of variance (ANOVA) filter approach is applied to improve feature selection (FS) more efficiently and highlight the most relevant features for dialect classification. The ANOVA filter method ranks features based on the means from different dialect groups, which made FS better. To make dialect classification work better, a 1D convolutional neural network model was given a dataset that had ANOVA FS added to it. The model showed a very strong performance, reaching a remarkable accuracy of 99.42%. This noteworthy increase in accuracy beat former research with an accuracy of 95.5%. The findings demonstrate how combining time stretch and FS methods can improve the accuracy of Kurdish dialect classification. This project improves our understanding and implementation of machine learning in the field of linguistic diversity and dialectology.