  Classifying Sorani Kurdish subdialects poses a challenge due to the need for
publicly available datasets or reliable resources like social media or websites
for data collection. We conducted field visits to various cities and villages
to address this issue, connecting with native speakers from different age
groups, genders, academic backgrounds, and professions. We recorded their
voices while engaging in conversations covering diverse topics such as
lifestyle, background history, hobbies, interests, vacations, and life lessons.
The target area of the research was the Kurdistan Region of Iraq. As a result,
we accumulated 29 hours, 16 minutes, and 40 seconds of audio recordings from
107 interviews, constituting an unbalanced dataset encompassing six
subdialects. Subsequently, we adapted three deep learning models: ANN, CNN, and
RNN-LSTM. We explored various configurations, including different track
durations, dataset splitting, and imbalanced dataset handling techniques such
as oversampling and undersampling. Two hundred and twenty-five(225) experiments
were conducted, and the outcomes were evaluated. The results indicated that the
RNN-LSTM outperforms the other methods by achieving an accuracy of 96%. CNN
achieved an accuracy of 93%, and ANN 75%. All three models demonstrated
improved performance when applied to balanced datasets, primarily when we
followed the oversampling approach. Future studies can explore additional
future research directions to include other Kurdish dialects.
