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首页> 外文会议>2012 IEEE Student Conference on Research and Development >Recognition system for nasal, lateral and trill arabic phonemes using neural networks
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Recognition system for nasal, lateral and trill arabic phonemes using neural networks

机译：使用神经网络的鼻，侧和三角阿拉伯音素识别系统

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There has been limited study and research in Arabic phoneme among Malaysians, hence making references to the work and research difficult. Although there have been significant acoustic and phonetic studies on languages such as English, French and Mandarin, to date there are no guidelines or significant findings on Malay language. In this paper, we monitored and analyzed the performance of multi-layer feed-forward with back-propagation (MLFFBP) and cascade-forward (CF) networks on our phoneme recognition system of Standard Arabic (SA). This study focused on Malaysian children as test subjects. Focused on four chosen phonemes from SA, which composed of nasal, lateral and trill behaviors, i.e. tabulated at four different articulation places. Highest training recognition rate for multi-layer and cascade-layer network are 98.8 % and 95.2 % respectively, while the highest testing recognition rate achieved for both networks is 92.9 %. 10-fold cross validation was used to evaluate system performance. The selected network is cascade layer with 40 and 10 hidden neurons in first hidden layer and second hidden layer respectively. The chosen network was used in the GUI designed for developing recognition system with user feedback.
机译：马来西亚人对阿拉伯语音素的研究和研究非常有限，因此很难对这项工作和研究进行参考。尽管对英语，法语和普通话等语言进行了大量的声学和语音研究，但迄今为止，还没有有关马来语言的指南或重要发现。在本文中，我们在标准阿拉伯语（SA）的音素识别系统上监视并分析了带有反向传播（MLFFBP）和级联前向（CF）网络的多层前馈的性能。这项研究的重点是马来西亚儿童作为测试对象。重点关注SA的四个选择的音素，这些音素由鼻，侧和颤音行为组成，即在四个不同的发音位置列表。多层和级联网络的最高训练识别率分别为98.8％和95.2％，而两个网络都达到的最高测试识别率为92.9％。 10倍交叉验证用于评估系统性能。所选择的网络是级联层，其中第一隐藏层和第二隐藏层分别具有40和10个隐藏神经元。所选网络用于GUI中，用于开发具有用户反馈的识别系统。

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