Native Language Identification (NLI) is a task in Natural Language Processing (NLP) that typically determines the native language of an author through their writing or a speaker through their speaking. It has various applications in different areas, such as forensic linguistics and general linguistics studies. Although considerable research has been conducted on NLI regarding two different languages, such as English and German, the literature indicates a significant gap regarding NLI for dialects and subdialects. The gap becomes wider in less-resourced languages such as Kurdish. This research focuses on NLI within the context of a subdialect of Sorani (Central) Kurdish. It aims to investigate the NLI for Hewlêri, a subdialect spoken in Hewlêr (Erbil), the Capital of the Kurdistan Region of Iraq. We collected about 24 hours of speech by recording interviews with 40 native or non-native Hewlêri speakers, 17 female and 23 male. We created three Neural Network-based models: Artificial Neural Network (ANN), Convolutional Neural Network (CNN), and Recurrent Neural Network (RNN), which were evaluated through 66 experiments, covering various time-frames from 1 to 60 seconds, undersampling, oversampling, and cross-validation. The RNN model showed the highest accuracy of 95.92% for 5-second audio segmentation, using an 80:10:10 data splitting scheme. The created dataset is the first speech dataset for NLI on the Hewlêri subdialect in the Sorani Kurdish dialect, which can be of benefit to various research areas.

翻译：母语识别是自然语言处理领域的一项任务，通常通过作者的书面文本或说话者的口语来确定其母语。该任务在多个领域具有应用价值，例如司法语言学与普通语言学研究。尽管针对英语、德语等不同语言的母语识别已有大量研究，但文献表明，针对方言及次方言的母语识别研究存在显著空白。对于库尔德语等资源稀缺的语言，这一空白更为突出。本研究聚焦于索拉尼（中部）库尔德语次方言背景下的母语识别，旨在探究伊拉克库尔德斯坦地区首府Hewlêr（埃尔比勒）使用的Hewlêri次方言的母语识别问题。我们通过采访40名Hewlêri母语及非母语使用者（17名女性、23名男性）录制了约24小时语音数据，构建了三种基于神经网络的模型：人工神经网络、卷积神经网络和循环神经网络，并通过66组实验进行评估，实验涵盖1至60秒不同时长片段、欠采样、过采样及交叉验证。采用80:10:10数据划分方案时，RNN模型在5秒音频分段上取得了95.92%的最高准确率。本研究构建的数据集是首个针对索拉尼库尔德语Hewlêri次方言的母语识别语音数据集，可为相关研究领域提供支持。