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Mehrsprachigkeit im Kontext des Kurmancî-Kurdischen und des Deutschen : eine Fallstudie aus einer kurdisch-deutschen Kindertagesstätte
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BLLDB
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UB Frankfurt Linguistik
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A multilingual corpus approach to postpredicativity in spoken Turkish, Kurmanji Kurdish and German
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In: Proceedings of the Workshop on Turkic and Languages in Contact with Turkic; Vol 6 (2021); 5068 ; 2641-3485 (2022)
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Jira: a Kurdish Speech Recognition System Designing and Building Speech Corpus and Pronunciation Lexicon
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In: https://hal.archives-ouvertes.fr/hal-03140680 ; 2021 (2021)
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CENTRAL KURDISH MACHINE TRANSLATION: FIRST LARGE SCALE PARALLEL CORPUS AND EXPERIMENTS
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In: https://hal.archives-ouvertes.fr/hal-03263105 ; 2021 (2021)
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Language practices and bi/plurilingual usages of Kurdish speakers in Istanbul ; Les pratiques langagières et les usages bi/plurilingues des kurdophones à Istanbul
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In: https://tel.archives-ouvertes.fr/tel-03611955 ; Linguistique. Normandie Université, 2021. Français. ⟨NNT : 2021NORMR104⟩ (2021)
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Kurdish spoken dialect recognition using x-vector speaker embeddings
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In: https://hal.archives-ouvertes.fr/hal-03262435 ; 2021 (2021)
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Abstract:
This paper presents a dialect recognition system for the Kurdish language using speaker embeddings. Two main goals are followed in this research: first, we investigate the availability of dialect information in speaker embeddings, then this information is used for spoken dialect recognition in the Kurdish language. Second, we introduce a public dataset for Kurdish spoken dialect recognition named Zar. The Zar dataset comprises 16,385 utterances in 49h-36min for five dialects of the Kurdish language (Northern Kurdish, Central Kurdish, Southern Kurdish, Hawrami, and Zazaki). The dialect recognition is done with x-vector speaker embedding which is trained for speaker recognition using Vox-celeb1 and Voxceleb2 datasets. After that, the extracted x-vectors are used to train support vector machine (SVM) and decision tree classifiers for dialect recognition. The results are compared with an i-vector system that is trained specifically for Kurdish spoken dialect recognition. In both systems (i-vector and x-vector), the SVM classifier with 86% of precision results in better performance. Our results show that the information preserved in the speaker embeddings can be used for automatic dialect recognition.
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Keyword:
[INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI]; [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing; dialect recognition; Kurdish language; Speaker embeddings; x-vector; Zar dataset
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URL: https://hal.archives-ouvertes.fr/hal-03262435/document https://hal.archives-ouvertes.fr/hal-03262435 https://hal.archives-ouvertes.fr/hal-03262435/file/Kurdish%20spoken%20dialect%20recognition.pdf
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Cross-dialectal diversity in Mukrī Kurdish I: phonological and phonetic variation [Online resource]
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In: Journal of Linguistic Geography 9.2021 (2021) 1, 1-12
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Linguistik-Repository
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WALS Online Resources for Kurmanji
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: Max Planck Institute for Evolutionary Anthropology, 2021
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WALS Online Resources for Kurdish (Central)
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: Max Planck Institute for Evolutionary Anthropology, 2021
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Glottolog 4.4 Resources for Northern Kurdish
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: Max Planck Institute for Evolutionary Anthropology, 2021
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Glottolog 4.4 Resources for Central Kurdish
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: Max Planck Institute for Evolutionary Anthropology, 2021
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Glottolog 4.4 Resources for Kurdish
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: Max Planck Institute for Evolutionary Anthropology, 2021
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Exploiting Script Similarities to Compensate for the Large Amount of Data in Training Tesseract LSTM: Towards Kurdish OCR
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In: Applied Sciences ; Volume 11 ; Issue 20 (2021)
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Glottolog 4.4 Resources for Southern Kurdish
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: Max Planck Institute for Evolutionary Anthropology, 2021
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Standard accented Turkish speakers’ perception of Kurdish accented speakers: The factors behind the evaluations
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In: Theses (2021)
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CHALLENGING NARRATIVES: KURDISH YOUNG ADULTS IN ISTANBUL AND CHICAGO
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In: Theses and Dissertations--Anthropology (2021)
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