Multilingual speech benchmarks such as the FLEURS benchmark have significantly advanced research across a wide range of languages. However, important dialects, including Badini Kurdish, remain underrepresented, limiting bechmarking in automatic speech recognition (ASR) and speech-to-text translation (S2TT). To address this limitation, this study introduces FLEURS-Badini, a dialect-focused extension designed to support research on Northern Kurdish (Badini). The dataset is constructed through a structured process of translation, recording, and validation, resulting in 5,224 utterances paired with their corresponding translated text. The data were collected from 45 speakers. To evaluate the dataset, baseline experiments are conducted using state-of-the-art models for both ASR and S2TT. The results indicate that ASR remains challenging, with the best performance achieved by the W2V-BERT CTC model, reaching a Word Error Rate (WER) of approximately 55% on the test set. Similarly, speech-to-text translation performance is limited, with BLEU scores 6.13 and 5.24 on dev and test sets. Overall, FLEURS-Badini expands multilingual coverage and provides a standardized foundation for evaluating ASR and speech translation systems in the Badini dialect.

Badini is a variant of the Kurdish language spoken in the Duhok province of the Kurdistan Region of Iraq. It is written mainly in a modified version of the Arabic script. Although it shares the same script as Central Kurdish (CKB), it is linguistically classified under the Northern Kurdish (KMR) branch. In this paper, we explore the potential and limitations of Northern Kurdish ASR resources for the Badini variant. Firstly, we transliterate the Common Voice 18 dataset from the Latin script into the modified Arabic script and revised it to align with the orthographic conventions of Badini variant. Additionally, we introduce the first text collection for the Badini variant, containing 14,22 million tokens, which serves as a source for speech synthesis. A third resource developed in this research is a standard speech recognition benchmark recorded by 5 speakers which includes 2 hours and 46 minutes of multi-domain read speech. Results show that combining transliterated and synthetic data significantly improves recognition accuracy, achieving a 6.8% CER and 34% WER. All three resources curated during this research will be made available under the CC BY-NC-ND 4.0 license.