A baseline system of keyword spotting based on continue hidden markov model ( chmm ) is constructed 研究的主要内容包括: 1 .基于连续隐马尔可夫模型( chmm )框架的非特定人关键词识别基线系统的构建。
Then we introduce some special structures in hidden markov models . second , we consider wavelet transformation and the related knowledge 然后还对隐马尔可夫模型中的一些特殊结构类型作了介绍。
As corollaries , several strong limit theorems about occurred frequency of states for hidden nonhomogeneous markov model are obtained 作为定理的推论,得到了隐非齐次马尔可夫模型状态出现频率的一类强极限定理。
The strong limit theorem of hidden nonhomogeneous markov model is studied when the hidden chains are nonhomogeneous markov chains 摘要假定隐藏的马尔可夫链为非齐次,研究隐非齐次马尔可夫模型的一些强极限定理。
Finally , a new method to adapt the parameters of hidden markov models is proposed when the input signals are transformed by wavelet 最后,我们提出了一种新的方法来解决通过小波变换后的隐马尔可夫过程参数的计算问题。
Pfam is a large collection of multiple sequence alignments and hidden markov models covering many common protein families 为一包含了许多多重序列校准,以及“隐藏式马尔科夫模型”的巨大集合,里面涵盖了许多常见的蛋白质家族。
A hidden markov model method ( hmm ) is used to recognize face in our automated recognition system . the element of hmm is introduced firstly 人脸识别方面,本文对在人脸识别中广泛应用的隐马尔可夫模型( hmm )的原理进行了介绍。
This article introduces the technology of information extraction based on hidden markov model that has been widely used in natural language domain 中医文献是中医学的主要知识资源,实现文献的数字化是信息时代中医文献研究的必然要求。
As a statistics model , hidden markov model ( hmm ) have been widely used in pattern recognition and stochastic signal processing 隐马尔科夫模型( hiddenmarkovmodel ,简记为hmm )作为一种统计模型,在模式识别与随机信号处理中有着广泛的应用。