We present KUTED, a speech-to-text translation (S2TT) dataset for Central Kurdish, derived from TED and TEDx talks. The corpus comprises 91,000 sentence pairs, including 170 hours of English audio, 1.65 million English tokens, and 1.40 million Central Kurdish tokens. We evaluate KUTED on the S2TT task and find that orthographic variation significantly degrades Kurdish translation performance, producing nonstandard outputs. To address this, we propose a systematic text standardization approach that yields substantial performance gains and more consistent translations. On a test set separated from TED talks, a fine-tuned Seamless model achieves 15.18 BLEU, and we improve Seamless baseline by 3.0 BLEU on the FLEURS benchmark. We also train a Transformer model from scratch and evaluate a cascaded system that combines Seamless (ASR) with NLLB (MT).

Polarity detection becomes substantially more challenging under domain shift, particularly in heterogeneous long-form narratives with complex discourse structure, such as Holocaust oral histories. This paper presents a corpus-scale diagnostic study of off-the-shelf sentiment classifiers on Holocaust oral histories, using three pretrained transformer-based polarity classifiers over a corpus comprising 107,304 utterances and 579,013 sentences. After assembling model outputs, we introduce an agreement-based stability taxonomy (ABC) to stratify inter-model output stability. We report pairwise percent agreement, Cohen’s κ, Fleiss’ κ, and row-normalized confusion matrices to localize systematic disagreement. As an external convergent descriptive signal, we apply a T5-based emotion classifier to stratified samples from each agreement stratum to compare emotion distributions across strata. The combination of multi-model label triangulation and the ABC taxonomy provides a cautious, interpretable framework for characterizing where and how sentiment models diverge in sensitive historical narratives. Inter-model agreement is low to moderate overall and is driven primarily by boundary decisions around neutrality.

We present FLEURS-Kobani, a Northern Kurdish (ISO 639-3 KMR) spoken extension of FLEURS benchmark. Although FLEURS offers n-way parallel speech for 100+ languages, Northern Kurdish is absent, limits benchmarking automatic speech recognition and speech translation tasks for this language. The FLEURS-Kobani dataset consists of 5,162 validated utterances, totaling 18 hours and 24 minutes. As baselines, we fine-tuned Whisper v3-large for ASR and E2E S2TT. A two-stage fine-tuning strategy (Common Voice→FLEURS-Kobani) yields the best ASR performance (WER 28.11, CER 9.84 on test). For end-to-end S2TT (KMR→EN), Whisper achieves 8.68 BLEU on test; we additionally report pivot-derived targets and a cascaded S2TT setup. FLEURS-Kobani provides the first Northern Kurdish public benchmark for evaluation of ASR, S2TT and S2ST tasks. The data can be accessed (LINK IS BLANK DUE TO REVISION RULES) under CC BY 4.0 license.

In this paper, we introduce the Kuvost, a large-scale English to Central Kurdish speech-to-text-translation (S2TT) dataset. This dataset includes 786k utterances derived from Common Voice 18, translated and revised by 230 volunteers into Central Kurdish. Encompassing 1,003 hours of translated speech, this dataset can play a groundbreaking role for Central Kurdish, which severely lacks public-domain resources for speech translation. Following the dataset division in Common Voice, there are 298k, 6,226, and 7,253 samples in the train, development, and test sets, respectively. The dataset is evaluated on end-to-end English-to-Kurdish S2TT using Whisper V3 Large and SeamlessM4T V2 Large models. The dataset is available under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License https://huggingface.co/datasets/aranemini/kuvost.

Kurdish, an Indo-European language spoken by over 30 million speakers, is considered a dialect continuum and known for its diversity in language varieties. Previous studies addressing language and speech technology for Kurdish handle it in a monolithic way as a macro-language, resulting in disparities for dialects and varieties for which there are few resources and tools available. In this paper, we take a step towards developing resources for language and speech technology for varieties of Central Kurdish, creating a corpus by transcribing movies and TV series as an alternative to fieldwork. Additionally, we report the performance of machine translation, automatic speech recognition, and language identification as downstream tasks evaluated on Central Kurdish subdialects. Data and models are publicly available under an open license at https://github.com/sinaahmadi/CORDI.