搜索资源列表
matlabkuozhanbiancheng
- matlab扩展编程——matlab扩展编程完整版，pdf格式，包含语音部分的两章的matlab例程-Matlab programming expansion -- Matlab version of the expansion program integrity, pdf format, part of the speech contains two chapters of Matlab routines
POS_tagging_and_HMM
- 词性标注与隐马尔可夫模型.ppt，相当好的说明材料。-part-of-speech tagging and Hidden Markov Model. Ppt, very good descr iptive material.
en
- 打开文件，获取英文词性，运用词对齐，实现对中文短语词性的输出。 -Open the file, access to the English part of speech, the use of the word alignment, part of speech to achieve the output of Chinese phrases.
HMM
- 北京大学汉语语言学研究中心下载资料（基于HMM的词性标记方法）-Linguistics Research Center, Beijing University of Chinese download data (the HMM-based part of speech tag method)
Chinese-Lexical-Analysis
- 一种基于层叠隐马模型的汉语词法分析方法，旨在将汉语分词、词性标注、切分排歧和未登录词识别集成到一个完整的理论框架中．-An approach for Chinese 1exical analysis using cascaded hidden Markav model, which aims to incorporate segmentation, part-of-speech tagging, disambiguation and unknown words recognition int
sheach
- 数据结构关于查找的算法、其中是以PPS的形式来呈现的、这一部分也是属于数据结构讲稿中-Data structure search algorithm, which is presented in the form of the PPS, this part is also the part of the data structure speech
hmm-tutorial
- The Hidden Markov Model (HMM) is a popular statistical tool for modelling a wide range of time series data. In the context of natural language processing(NLP), HMMs have been applied with great success to problems such as part-of-speech tagging a
speech-modeling-based-on-HNM.pdf
- this paper presents a speech modeling method that supposes speech to be formed of harmonic and noise part.
Voice_Recognition_System_in_Noisy_Environment
- voice recognition in noisy environment Human life could be much more comfortable, if global machinery works on voice commands. There are lot of products available in the market those provide us automated voice controlled working experience. Voic
2808-14159-1-PB
- In this paper, we systematically explore feature definition and selection strategies for sentiment polarity classification. We begin by exploring basic questions, such as whether to use stemming, term frequency versus binary weighting, negation-enr