Kuvost: A Large-Scale Human-Annotated E nglish to C entral K urdish Speech Translation Dataset Driven from E nglish Common Voice (2025)

Abstract: 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.

## introduction speech translation is the automatic conversion of audio from a source language into text or audio in a target language @xcite . developing a speech-to-text translation system requires large amounts of translated audio; however, most languages lack sufficient data in this area. in @xcite , languages with fewer than 1,000 hours of publicly available transcribed or translated data are classified as low-resource. by this definition, only about a dozen out of 7,000 languages qualify as high-resource. providing speech translation data-especially for low-resource languages-is therefore crucial for progress in this field. in this paper, we introduce a speech-to-text translation (s2tt) dataset for central kurdish (ckb), which is a low-resource language @xcite . this dataset, called kuvost (kurdish common voice speech translation), is derived from the common voice 18 dataset. the kuvost dataset contains 247k unique sentences translated by 230 volunteers and passed through a systematic revision process. due to multiple recordings for some of the translated sentences in common voice, the total audio duration in kuvost amounts to 1,003 hours. extending automatic speech recognition datasets by translating their transcriptions is a common strategy for building speech translation corpora. cov-ost @xcite and covost 2 @xcite are two well-known examples, both derived from common voice. covost 2 is currently one of the large-scale publicly available speech translation corpus, including english-to-15 languages and 21-to-english s2tt pairs @xcite . aug-librispeech is a french-translated version of the librispeech corpus, comprising a 236-hour @xmath0. voxpopuli is a multi-way speech translation corpus based on european parliament (ep) event recordings, encompassing 15 european languages @xcite . ted talks and tedx have also been widely used for speech translation. the must-c dataset contains english-to-14-language s2tt data derived from ted talks @xcite . tedx includes translations from english to 7 languages, while indic-tedst is the third ted-derived corpus, featuring translations from english to 9 indian languages @xcite . the fleurs dataset is currently the most comprehensive speech translation dataset in terms of the number of covered languages. fleurs is a multi-way text-to-text and speech-to-speech corpus for 101 languages. it is also the only speech translation dataset that includes the central kurdish language-the subject of the current research @xcite . the goal of this paper is to fill the gap in speech translation data scarcity for the central kurdish language. in this paper, we focus on central kurdish, which is spoken by nearly 8 million native speakers @xcite . central kurdish is a statutory national language in iraq 1 and a de facto provincial working language in iran 2 . although recent years have seen notable progress in data curation and system development for central kurdish, the speech translation domain from/to this dialect remains largely unexplored. the goal of this paper is to address this gap. ## translation process the data for translation was sourced from common voice 18 3 . the data creation process consisted of three main steps: (1) announcement and recruitment of volunteers, (2) training and data distribution, and (3) translation and review. announcement and recruitment of volunteers: an announcement was made in june 2024 to recruit volunteers who study english at the department of english language(denl) at koya university. a total of 259 volunteers-mostly third-and fourth-year students-signed up. all volunteers were native speakers of central kurdish. training: a two-week intensive training program was then offered to the volunteers. during this program, participants were introduced to various translation techniques. additionally, a detailed guideline outlining the rules of translation was provided. after the training workshops, volunteers were given the option to withdraw without providing a reason. at this stage, 17 volunteers dropped out. the remaining participants were divided into three main groups, each supervised by a faculty 1 https://www.ethnologue.com/country/iq/ 2 https://www.ethnologue.com/country/ir/ 3 https://commonvoice.mozilla.org/en/datasets member. these were further divided into smaller sub-groups of five volunteers. the translation data was distributed via google sheets, with access provided to both volunteers and supervisors. revision: the review process involved two main stages: furthermore, weekly seminars were also held to address common mistakes and discuss correction strategies. during the revision phase, an additional 12 volunteers dropped out. by the end of the process, a total of 230 volunteers, plus 7 kurdish language reviewers, had fully or partially completed their tasks, translating 247,373 sentences. ## kuvost statistics the statistics of the kuvost dataset are presented in the validated and translated utterances were divided into train, development, and test sets according to the original common voice 18 partitioning. for each split, we referred to the common voice 18 train/dev/test sets and matched the english transcriptions with their corresponding kurdish translations. the training set includes 298k utterances, equivalent to 417 hours of audio. the development and test sets each contain approximately 9 hours of translated speech. it deserved to be mentioned that all validated examples in the common voice are not included in the train/dev/test partitions which leads to lower number of utterances in the partitions. ## evaluation systems the kuvost dataset is evaluated by fine-tuning two state-of-the-art speech translation models: whisper v3 (wl v3) large and seamlessm4t v2 (sl v2) large models. ## conclusion in this paper, we introduced kuvost, a large-scale, human-annotated speech translation dataset for central kurdish. kuvost consists of 1,003 hours of english-to-kurdish speech translation, contributed by 230 volunteers. the dataset is evaluated using state-of-the-art speech translation models. for future work, we plan to record kurdish translations to extend kuvost for speech-to-speech translation tasks. additionally, we aim to expand the dataset to support kurdish-to-x translation for all languages available in the covost 2 dataset @xcite .