为构造出负整数次幂复映射族f(z)=z^(-n)+c的新型分形,研究了利用该复映射族的广义M集的1周期参数构造非线性迭代函数系的方法.根据广义M集的对称性,选定正实轴上方与正实轴成π/(n+1)角度内的M集中1周期参数区域为构造IFS(Iterated Fun...为构造出负整数次幂复映射族f(z)=z^(-n)+c的新型分形,研究了利用该复映射族的广义M集的1周期参数构造非线性迭代函数系的方法.根据广义M集的对称性,选定正实轴上方与正实轴成π/(n+1)角度内的M集中1周期参数区域为构造IFS(Iterated Function Systems,函数迭代系)中压缩迭代函数的参数源区域;试验选取2个或以上参数构造非线性压缩IFS,并构造分形.根据完整M集的1周期参数区域的对称特点,在参数源区域挑选多个参数,将每个参数扩展到n+1个旋转对称参数或2(n+1)个旋转对称和反射对称参数,由这些参数构造出相应的迭代函数,组成一个非线性的IFS,并构造出对称分形.实验结果表明,用本文方法构造的非线性IFS,可以用复映射族f(z)=z^(-n)+c构造出大量的结构各异的新颖分形.展开更多
A framework for dialectal Chinese speech recognition is proposed and studied, in which a relatively small dialectal Chinese （or in other words Chinese influenced by the native dialect） speech corpus and dialect-rela...A framework for dialectal Chinese speech recognition is proposed and studied, in which a relatively small dialectal Chinese （or in other words Chinese influenced by the native dialect） speech corpus and dialect-related knowledge are adopted to transform a standard Chinese （or Putonghua, abbreviated as PTH） speech recognizer into a dialectal Chinese speech recognizer. Two kinds of knowledge sources are explored： one is expert knowledge and the other is a small dialectal Chinese corpus. These knowledge sources provide information at four levels： phonetic level, lexicon level, language level, and acoustic decoder level. This paper takes Wu dialectal Chinese （WDC） as an example target language. The goal is to establish a WDC speech recognizer from an existing PTH speech recognizer based on the Initial-Final structure of the Chinese language and a study of how dialectal Chinese speakers speak Putonghua. The authors propose to use contextindependent PTH-IF mappings （where IF means either a Chinese Initial or a Chinese Final）, context-independent WDC-IF mappings, and syllable-dependent WDC-IF mappings （obtained from either experts or data）, and combine them with the supervised maximum likelihood linear regression （MLLR） acoustic model adaptation method. To reduce the size of the multipronunciation lexicon introduced by the IF mappings, which might also enlarge the lexicon confusion and hence lead to the performance degradation, a Multi-Pronunciation Expansion （MPE） method based on the accumulated uni-gram probability （AUP） is proposed. In addition, some commonly used WDC words are selected and added to the lexicon. Compared with the original PTH speech recognizer, the resulting WDC speech recognizer achieves 10-18% absolute Character Error Rate （CER） reduction when recognizing WDC, with only a 0.62% CER increase when recognizing PTH. The proposed framework and methods are expected to work not only for Wu dialectal Chinese but also for other dialectal Chinese languages and even other languages.展开更多
文摘为构造出负整数次幂复映射族f(z)=z^(-n)+c的新型分形,研究了利用该复映射族的广义M集的1周期参数构造非线性迭代函数系的方法.根据广义M集的对称性,选定正实轴上方与正实轴成π/(n+1)角度内的M集中1周期参数区域为构造IFS(Iterated Function Systems,函数迭代系)中压缩迭代函数的参数源区域;试验选取2个或以上参数构造非线性压缩IFS,并构造分形.根据完整M集的1周期参数区域的对称特点,在参数源区域挑选多个参数,将每个参数扩展到n+1个旋转对称参数或2(n+1)个旋转对称和反射对称参数,由这些参数构造出相应的迭代函数,组成一个非线性的IFS,并构造出对称分形.实验结果表明,用本文方法构造的非线性IFS,可以用复映射族f(z)=z^(-n)+c构造出大量的结构各异的新颖分形.
基金This paper is based upon a study supported by the US National Science Foundation under Grant No.0121285. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author（s） and do not necessarily reflect the views of the National Science Foundation.
文摘A framework for dialectal Chinese speech recognition is proposed and studied, in which a relatively small dialectal Chinese （or in other words Chinese influenced by the native dialect） speech corpus and dialect-related knowledge are adopted to transform a standard Chinese （or Putonghua, abbreviated as PTH） speech recognizer into a dialectal Chinese speech recognizer. Two kinds of knowledge sources are explored： one is expert knowledge and the other is a small dialectal Chinese corpus. These knowledge sources provide information at four levels： phonetic level, lexicon level, language level, and acoustic decoder level. This paper takes Wu dialectal Chinese （WDC） as an example target language. The goal is to establish a WDC speech recognizer from an existing PTH speech recognizer based on the Initial-Final structure of the Chinese language and a study of how dialectal Chinese speakers speak Putonghua. The authors propose to use contextindependent PTH-IF mappings （where IF means either a Chinese Initial or a Chinese Final）, context-independent WDC-IF mappings, and syllable-dependent WDC-IF mappings （obtained from either experts or data）, and combine them with the supervised maximum likelihood linear regression （MLLR） acoustic model adaptation method. To reduce the size of the multipronunciation lexicon introduced by the IF mappings, which might also enlarge the lexicon confusion and hence lead to the performance degradation, a Multi-Pronunciation Expansion （MPE） method based on the accumulated uni-gram probability （AUP） is proposed. In addition, some commonly used WDC words are selected and added to the lexicon. Compared with the original PTH speech recognizer, the resulting WDC speech recognizer achieves 10-18% absolute Character Error Rate （CER） reduction when recognizing WDC, with only a 0.62% CER increase when recognizing PTH. The proposed framework and methods are expected to work not only for Wu dialectal Chinese but also for other dialectal Chinese languages and even other languages.
