基于决策树的藏语拉萨话三音子模型

J4 ›› 2013, Vol. 35 ›› Issue (9): 146-150.

基于决策树的藏语拉萨话三音子模型

李冠宇，于洪志，李永宏，马宁

(西北民族大学中国民族语言文字教育部重点实验室，甘肃兰州 730030)

收稿日期:2013-04-16 修回日期:2013-07-26 出版日期:2013-09-25 发布日期:2013-09-25
基金资助:
国家自然科学基金资助项目(61262054);西北民族大学中央高校基本科研业务费专项资金项目(zyz2011100)

Triphone models of Lhasa Tibetan based on decision tree

LI Guanyu,YU Hongzhi,LI Yonghong,MA Ning

(Key Laboratory for Chinese Ethnic Minority Language of Ministry of Education,
Northwest University for Nationalities,Lanzhou 730030,China)

Received:2013-04-16 Revised:2013-07-26 Online:2013-09-25 Published:2013-09-25

摘要/Abstract

摘要：

对藏语拉萨话中单音子及三音子分布情况进行了统计，分析了在藏语大词表连续词表连续语音识别中建立上下文相关声学模型的必要性。选择音素为建模单元，根据藏语特点，建立以音节为单位的发音字典。讨论了利用决策树建立三音子模型的几个关键问题和基本算法，结合国际音标分类和经验知识，确定了38个藏语拉萨话音子类别集及相应的决策树问题集。建立了共20个发音人8170句的训练语料，在HTK平台上建立和训练得到了基于决策树的藏语拉萨话三音子模型，并分析了不同隐马尔可夫模型状态数及高斯混合度下的识别结果，确定了一套藏语大词表连续语音识别的完整方案。

关键词: 藏语, 拉萨话, 大词表连续语音识别, 隐马尔可夫模型, 三音子模型

Abstract:

Probability distribution of monophones and triphones in Lhasa Tibetan are calculated and the necessity of establishing a contextual acoustic model in ASR for Lhasa Tibetan is analyzed. Phoneme is chosen as basic unit for acoustic models. According to the characteristics of Tibetan, a pronunciation dictionary based on single syllable is established. Main issues and algorithms for triphone models based on decision tree are discussed. According to IPAs and characteristics of Lhasa dialect, 38 phoneme subsets and question sets for triphone modeling are established. 8170 sentences of 20 speakers are recorded to train the models. Contextual continuous Hidden Markov Models(HMM) based on triphones are established and trained on HTK platform. The recognition results under different sates number and mixtures are analyzed. And the framework for largevocabulary continuous speech recognition of Lhasa Dialect is established.

Key words: Tibetan;Lhasa dialect;LVCSR;HMM;triphone model

李冠宇，于洪志，李永宏，马宁. 基于决策树的藏语拉萨话三音子模型[J]. J4, 2013, 35(9): 146-150.

LI Guanyu,YU Hongzhi,LI Yonghong,MA Ning. Triphone models of Lhasa Tibetan based on decision tree [J]. J4, 2013, 35(9): 146-150.

[1]	王会举, 李孟萱, 黄卫卫, 周秋怡. 基于隐马尔可夫模型的多真值发现算法[J]. 计算机工程与科学, 2021, 43(03): 518-524.
[2]	夏吾吉1,2，华却才让1. 基于投射的藏语语义依存分析研究[J]. 计算机工程与科学, 2019, 41(10): 1868-1873.
[3]	周雁,西绕多吉. 面向藏语声纹识别的语料库建设[J]. 计算机工程与科学, 2018, 40(11): 2080-2084.
[4]	李娟1，张冰怡1，冯志勇1，徐超2，张铮3. 基于隐马尔可夫模型的视频异常场景检测[J]. 计算机工程与科学, 2017, 39(07): 1300-1308.
[5]	李冠宇，于洪志，吴志强. 一种语料缺乏条件下的藏语音素自动切分方法[J]. J4, 2014, 36(10): 2009-2013.
[6]	包亚萍,郑〓骏,武晓光. 基于HMM和遗传神经网络的语音识别系统[J]. J4, 2011, 33(4): 139-144.
[7]	孙一品钟求喜苏金树. 基于隐马尔可夫模型的攻击意图识别技术研究[J]. J4, 2007, 29(8): 19-22.
[8]	吴君浩[1] 骆嘉伟[1] 王艳[1] 杨涛[1] 杨旭[2]. 基于隐马尔可夫模型的二次k-均值基因序列聚类算法[J]. J4, 2007, 29(3): 54-56.
[9]	施德明林洋港陈恩红. 一种集成NER的文本分类特征选择方法[J]. J4, 2007, 29(11): 152-156.
[10]	张晓艳王挺陈火旺. 基于混合统计模型的汉语命名实体识别方法[J]. J4, 2006, 28(6): 135-139.
[11]	卢正鼎董泽锋. 文法推断与HMM相结合的信息提取[J]. J4, 2005, 27(8): 1-3.