| 基于动态贝叶斯网络的汉语方言辨识 |
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| 引用本文： | 周杰,顾明亮,张宁,杨帆. 基于动态贝叶斯网络的汉语方言辨识[J]. 计算机技术与发展, 2012, 0(11): 179-182 |
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| 作者姓名： | 周杰 顾明亮 张宁 杨帆 |
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| 作者单位： | [1]徐州师范大学物理与电子工程学院,江苏徐州221116 [2]徐州师范大学语言科学研究所,江苏徐州221116 |
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| 基金项目： | 国家自然科学基金（61040053） |
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| 摘 要： | 方言的差异性在语音层面上反映在时间序列结构的不同。传统的语音建模方法只能建立稳定的时间序列结构，而方言语音是典型的动态时变时间序列结构。为了更好地提取方言时间序列结构，文中采用动态贝叶斯网路（DBN）进行建模分析，并对DBN的构建方法进行了研究，这种结构与常用于语音识别中的隐马尔可夫模型的不同之处在于它揭示多个时间片内的节点之间的影响。文中探索了不同结构和参数对识别效果的影响。文中的研究表明动态贝叶斯网络对汉语方言的识别比传统方法要好，识别率达到了98．9％。
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| 关 键 词： | 动态贝叶斯网络 汉语方言辨识 联合树算法 |
Chinese Dialect Identification Based on DBN |
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| ZHOU Jie,GU Ming-liang,ZHANG Ning,YANG Fan. Chinese Dialect Identification Based on DBN[J]. Computer Technology and Development, 2012, 0(11): 179-182 |
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| Authors: | ZHOU Jie GU Ming-liang ZHANG Ning YANG Fan |
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| Affiliation: | 1. School of Physics ＆ Electronic Engineering, Xuzhou Normal University, Xuzhou 221116, China; 2. School of Linguistic Science, Xuzhou Normal University, Xuzhou 221116, China) |
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| Abstract: | The differentiation of Chinese dialect is the different time series in the phonetic. Traditional speech modeling methods can only establish time series,but the dialect speech is typical time-varying series. It chose dynamic Bayesian networks to model the speech in or der to extract the time series structure of dialect speech. It also studied the method to model the DBN structure and the influence of the model complexity on recognition rate. The structures of this paper is more complex than the HMM because these structures notice the in fluence of the nodes in more than two time series. Experiments show that the DBN method is an excellent method with high rate 98.9%. |
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| Keywords: | dynamic Bayesian networks （DBN） Chinese dialect identification junction tree algorithm |
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