Abstract
This paper presents a dialect recognition system for the Kurdish language using speaker embedding. Two main goals are followed in this research: first, we investigate the availability of dialect information in speaker embedding, 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 49 h-36 min 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 Voxceleb1 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 87% of precision results in better performance. Our results show that the information preserved in the speaker embedding can be used for automatic dialect recognition.
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Amani, A., Mohammadamini, M., Veisi, H. (2021). Kurdish Spoken Dialect Recognition Using X-Vector Speaker Embedding. In: Karpov, A., Potapova, R. (eds) Speech and Computer. SPECOM 2021. Lecture Notes in Computer Science(), vol 12997. Springer, Cham. https://doi.org/10.1007/978-3-030-87802-3_5
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