使用1D CNN的库尔德方言识别

IF 1.2 Q3 MULTIDISCIPLINARY SCIENCES
Karzan J. Ghafoor, Karwan M. Hama Rawf, A. O. Abdulrahman, Sarkhel H. Taher
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引用次数: 9

摘要

方言识别是语音分析领域最受关注的话题之一。机器学习算法已被广泛用于识别方言。本文基于三种不同的一维卷积神经网络(CNN)结构,建立了库尔德语方言识别模型。对该模型进行了评估,并对CNN结构进行了比较。结果表明,该模型的性能优于现有的模型。利用哈拉布贾大学计算机系工作人员收集的实验数据对模型进行了评价。数据集中涉及三种方言,因为库尔德语由三种主要方言组成,即北部库尔德语(巴迪尼变体),中部库尔德语(索拉尼变体)和哈瓦拉米语。CNN模型的优点是不需要关注手工,因为CNN模型是无特征的。结果表明,一维CNN方法对库尔德语方言分类的预测平均准确率为95.53%。在这项研究中,提出了一种新的方法,通过混淆矩阵和非度量的多维可视化技术来解释库尔德方言的亲密性。结果表明,对给定的库尔德方言进行聚类很简单,并且与邻近的方言线性分离。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Kurdish Dialect Recognition using 1D CNN
Dialect recognition is one of the most attentive topics in the speech analysis area. Machine learning algorithms have been widely used to identify dialects. In this paper, a model that based on three different 1D Convolutional Neural Network (CNN) structures is developed for Kurdish dialect recognition. This model is evaluated, and CNN structures are compared to each other. The result shows that the proposed model has outperformed the state of the art. The model is evaluated on the experimental data that have been collected by the staff of department of computer science at the University of Halabja. Three dialects are involved in the dataset as the Kurdish language consists of three major dialects, namely Northern Kurdish (Badini variant), Central Kurdish (Sorani variant), and Hawrami. The advantage of the CNN model is not required to concern handcraft as the CNN model is featureless. According to the results, the 1 D CNN method can make predictions with an average accuracy of 95.53% on the Kurdish dialect classification. In this study, a new method is proposed to interpret the closeness of the Kurdish dialects by using a confusion matrix and a non-metric multi-dimensional visualization technique. The outcome demonstrates that it is straightforward to cluster given Kurdish dialects and linearly isolated from the neighboring dialects.
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来源期刊
ARO-THE SCIENTIFIC JOURNAL OF KOYA UNIVERSITY
ARO-THE SCIENTIFIC JOURNAL OF KOYA UNIVERSITY MULTIDISCIPLINARY SCIENCES-
自引率
33.30%
发文量
33
审稿时长
16 weeks
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