数据挖掘及其在汉语文语转换中应用的研究
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摘要
为了满足人们实际工作中的需要，数据库中的知识发现近年来逐渐发展起来。本文对数据库中的知识发现处理过程模型进行了研究，并将数据挖掘应用于普通话韵律规则发现，取得了良好的效果。
目前进行的数据挖掘的研究目前所进行的关于KDD的研究，大多只着眼于对学习算法的研究，而忽视了整个处理过程的研究。基于上述因素，本文提出了支持多数据集多学习目标的KDD处理过程模型，以使得KDD更适合实际工作的需要并使得最终用户和数据挖掘人员的之间的影响尽量小，提高学习效率。
目前的合成语音的自然度和连续度不够高，影响合成语音质量不高的一个很重要因素在于目前所使用的韵律控制规则不够完善。本文提出在基音同步叠加基础上利用数据挖掘进行汉语韵律规则的学习，并在汉语两字词和句子的韵律规则学习中得到应用，收到了良好的效果，目前国内外有关语音合成的文献中未见同类成果的报道。
本文将两字词中的音节韵律规则看做是对孤立音节和词中发音的一种映射关系的描述，通过训练神经网络获取基频和时长的映射关系，利用训练后得到的网络直接计算出所需的基频以用于合成，通过实验，得到较好的学习效果并且利用神经网络生成的基频变化结果完全符合公认的声调变化规律。
为了学习句子中音节的韵律变化规律，本文首先通过聚类分析得出典型的句中音节基频模式，这些基频模式完全可以对应于目前通用的声调曲线。在基频模式基础上，本文将训练数据中的基频变换到高层次描述并综合采用多种数据挖掘方法进行韵律规则的学习，取得了较好的实验结果。通过学习所获取的变化规则完全包容了变调规则，而且产生的新规则对声调变化的研究也将起到一定的启发作用。
在上述工作的基础上，本文开发出基于数据挖掘的普通话文语转换的研究原型系统DMTalker，该系统利用数据挖掘进行韵律规则学习，并将学习得到的韵律规则用于文语转换中。
To meet the requirement, Knowledge Discovery in Database(KDD) comes into being these years. This thesis have done research on the KDD process model, and finding prosodic rules for Mandarin Speech synthesis by data mining. The results are encouraging.
KDD has been caught more and more attention recently, but most of the current research on KDD pay much attention to data mining, which is one stage of KDD, and little to the KDD process model. But actually data mining has been done little amount of mining work of the whole discovery task. Reasonable KDD process model can organize the whole discovery stages into an solid unit, and thus makes it easy for end users to use KDD.
We propose a KDD process model which is based on the analysis of the practice, and it supports dataset and multi-thread training. The proposed model is more suitable for the KDD application, and it makes the influence between data mining expert and end user as little as possible, so it can make knowledge discovery more efficient.
The current synthesized speech has low quality, and one of the reasons is that the prosodic rules which are now being used are unsatisfied. We propose to learn prosodic models by data mining from actual speech database, and it was implemented in the learning from phrases and sentences.
To extract pitch variation patterns from two-word phrases, a data mining system called SpeechDM has been implemented. In Chinese, the pitches extracted from an isolate syllable differ from those extracted from the same syllable in phrases, and SpeechDM extracts the patterns from the mapping between them. Since the pitch variation patterns have been learned from actual speech, it is possible to improve the naturalness of synthesized speech.
The pitch models which are now being used in Mandarin Text-To-Speech are extracted by linguistics experts, and they are described qualitatively and with low precise. To acquire more accurate prosodic rules, data mining is
目前进行的数据挖掘的研究目前所进行的关于KDD的研究，大多只着眼于对学习算法的研究，而忽视了整个处理过程的研究。基于上述因素，本文提出了支持多数据集多学习目标的KDD处理过程模型，以使得KDD更适合实际工作的需要并使得最终用户和数据挖掘人员的之间的影响尽量小，提高学习效率。
目前的合成语音的自然度和连续度不够高，影响合成语音质量不高的一个很重要因素在于目前所使用的韵律控制规则不够完善。本文提出在基音同步叠加基础上利用数据挖掘进行汉语韵律规则的学习，并在汉语两字词和句子的韵律规则学习中得到应用，收到了良好的效果，目前国内外有关语音合成的文献中未见同类成果的报道。
本文将两字词中的音节韵律规则看做是对孤立音节和词中发音的一种映射关系的描述，通过训练神经网络获取基频和时长的映射关系，利用训练后得到的网络直接计算出所需的基频以用于合成，通过实验，得到较好的学习效果并且利用神经网络生成的基频变化结果完全符合公认的声调变化规律。
为了学习句子中音节的韵律变化规律，本文首先通过聚类分析得出典型的句中音节基频模式，这些基频模式完全可以对应于目前通用的声调曲线。在基频模式基础上，本文将训练数据中的基频变换到高层次描述并综合采用多种数据挖掘方法进行韵律规则的学习，取得了较好的实验结果。通过学习所获取的变化规则完全包容了变调规则，而且产生的新规则对声调变化的研究也将起到一定的启发作用。
在上述工作的基础上，本文开发出基于数据挖掘的普通话文语转换的研究原型系统DMTalker，该系统利用数据挖掘进行韵律规则学习，并将学习得到的韵律规则用于文语转换中。
To meet the requirement, Knowledge Discovery in Database(KDD) comes into being these years. This thesis have done research on the KDD process model, and finding prosodic rules for Mandarin Speech synthesis by data mining. The results are encouraging.
KDD has been caught more and more attention recently, but most of the current research on KDD pay much attention to data mining, which is one stage of KDD, and little to the KDD process model. But actually data mining has been done little amount of mining work of the whole discovery task. Reasonable KDD process model can organize the whole discovery stages into an solid unit, and thus makes it easy for end users to use KDD.
We propose a KDD process model which is based on the analysis of the practice, and it supports dataset and multi-thread training. The proposed model is more suitable for the KDD application, and it makes the influence between data mining expert and end user as little as possible, so it can make knowledge discovery more efficient.
The current synthesized speech has low quality, and one of the reasons is that the prosodic rules which are now being used are unsatisfied. We propose to learn prosodic models by data mining from actual speech database, and it was implemented in the learning from phrases and sentences.
To extract pitch variation patterns from two-word phrases, a data mining system called SpeechDM has been implemented. In Chinese, the pitches extracted from an isolate syllable differ from those extracted from the same syllable in phrases, and SpeechDM extracts the patterns from the mapping between them. Since the pitch variation patterns have been learned from actual speech, it is possible to improve the naturalness of synthesized speech.
The pitch models which are now being used in Mandarin Text-To-Speech are extracted by linguistics experts, and they are described qualitatively and with low precise. To acquire more accurate prosodic rules, data mining is
引文
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[2] J. Han, Y. Fu, K. Koperski, G. Melli, W. Wang, O. R. Za 颽 ne, "Knowledge Mining in Databases: An Integration of Machine Learning Methodologies with Database Technologies", Canadian Artificial Intelligence, October 1995.
[3] J. Hart, "Data Mining Techniques", Proc. 1996 ACM-SIGMOD Int'l Conf. on Management of Data (SIGMOD'96)}, Montreal, Canada, June 1996
[4] R. Agrawal, T. Imielinski, A. Swami: "Database Mining: A Performance Perspective", IEEE Transactions on Knowledge and Data Engineering, Special issue on Learning and Discovery in Knowledge-Based Databases, Vol. 5, No. 6, December 1993, 914-925.
[5] M.-S. Chen, J. Han, and P.S. Yu, "Data Mining: An Overview from Database Perspective", IEEE Transactions on Knowledge and Data Engineering, 1997.
[6] Kero, B, L. Russell, S. Tsur and W.M. Shen An Overview of Data Mining Technologies The KDD Workshop in the 4th International Conference on Deductive and Object-Oriented Databases, Singapore, 1995.
[7] George H.John , (1997). Enhancements to the Data Mining Process. Ph.D thesis of Stanford University.
[8] 蔡连红，涂相华，(1996)．音频合成的基本原理与实现．微型计算机．Vol．16，No．4．
[9] 初敏，(1995)．高清晰度高自然度汉语文语转换系统的研究．中国科学院声学研究所博士学位论文．
[10] 刘庆峰，倪晋富，王仁华，(1997)．提高合成语音自然度的研究．第三届中国计算机智能接口与智能应用学术会议论文集，p163-168．
[11] 吴宗济，(1982)．普通话语句中的声调变化．中国语文．1982年第六期，p439-449．
[12] 冯隆，(1985)．北京话语流中声韵调的时长．北京语音实验录．北京大学出版社．P131-195．
[13] 王军，(1997)．数据库知识发现的研究．中国科学院软件所博士学位论文．
[14] P. Brazdil and P. Clark. Learning from Imperfect Data. In P. B. Brazdil and K. Konolige, editors, Machine Learning, Meta-reasoning and Logics, pages 207-232, Boston, 1990. Kluwer.
[15] Cormack, R.M.1971. A review of classification. J. Roy. Statist. Soc. Serie A, 134:321-367.Sreerama K. Murthy (1995). On Growing Better Decision Trees from Data Ph.D. thesis, October 1995
[16] J. Han, Y. Cai and N. Cercone, "Concept-Based Data Classification in Relational Databases", in Workshop Notes of 1991 AAAI Workshop on Knowledge Discovery in Databases (KDD'91), Anaheim, CA, July 1991, pp. 77-94.
[17] A Weighted Nearest Neighbor Algorithm for Learning with Symbolic Features. By S. Cost and S. Salzberg. Machine Learning 10:1, 1993, 57—78.
[18] "Data Clustering and Learning," J. Buhmann, In Handbook of Brain Theory and Neural Networks, M. Arbib, ed., Bradfort Books/MIT Press, 1995.
[19] Anderberg, M.R. 1973. Cluster Analysis for Applications. Academic Press.
[20] Everitt, B. 1974. Cluster Analysis. Heinemann Educational Books Ltd.
[21] Kaufman, L. and Rousseeuw, P.J. 1990. Finding Groups in Data-An Introduction to Cluster Analysis. Wiley.
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