编码的识别技术用于识别语音信号是隐马尔科夫模型的技术。
The recognition technique used for the recognition of the coded speech signals is the Hidden Markov Model technique.
然后，在分词之后引入隐马尔科夫模型来识别大部分音乐实体。
Then after word segmentation, we introduced Hidden Markov Model to identify most of musical entities.
在实践中，这是一个马尔科夫模型对与实际过程的一个最不现实的假设之一。
In practice, this is one of the most unrealistic assumptions of Markov models about real processes.
方法：阐述马尔科夫模型的原理，通过实例说明其在药品经济预测中的应用。
METHOD: the principle of Markov model was expounded and its application in the forecasting of drug market was illustrated with examples.
提出一种结合分层隐马尔科夫模型(LHMM)与熵值的聚众事件实时检测方法。
This paper proposes a Layered Hidden Markov Model (LHMM) and entropy method to detect a gathering event in real-time.
在某些情况下，甲基化的酶作用物能够从隐马尔科夫模型序列相似性网络中被推断。
In some cases, the substrate of methylation can be inferred from hidden Markov model sequence similarity networks.
介绍了一个基于隐马尔科夫模型的、采用模糊分割方式的脱机手写英文单词识别系统。
Our off-line cursive English word recognition system was based on HMM (Hidden Markov Models) and blur segmentation.
为了推理移动用户在智能空间的活动，提出了基于隐马尔科夫模型的上下文感知活动计算。
The context-aware activity computing based on hidden Markov model was proposed to predict the activity of mobile user in a smart space.
该文研究了基于数据模拟方法和HMM（隐马尔科夫模型）自适应的电话信道条件下语音识别问题。
This paper addresses the problem of speech recognition under telephone channel conditions using data simulation method and HMM(Hidden Markov Model)adaptation.
这种类型的问题出现在需要使用大量马尔科夫模型的语言识别中，每一个模型对特定的一个词建模。
This type of problem occurs in speech recognition where a large number of Markov models will be used, each one modelling a particular word.
结果：马尔科夫模型是一种具有很多优点的预测方法，能够对符合假设条件下的各种经济动态进行准确预测。
RESULT:Markov model was a forecasting method of many merits, which can be used to forecast those economy dynamic states that were in line with the condition of hypothesis.
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。
Pfam is a large collection of multiple sequence alignments and hidden Markov models covering many common protein families.
同时，我们提出了一种新颖、实用的训练语料标注方案，这使得隐马尔科夫模型在音乐实体识别上变得实际可行。
Meanwhile, a novel and convenient training corpus tagging method was proposed, which made Hidden Markov Model practically usable in Musical Entity Recognition.
通过建立马尔科夫模型，分析了双机系统的可用性，研究了故障检测覆盖率和故障诊断成功率对系统可用性的影响。
The markov model is applied to analyze the influence of fault detection coverage and diagnosis success rate for system availability.
在实验中，将该算法分别应用到实值离散小波变换域和双树复数小波变换域，并和隐马尔科夫模型的去噪方法做了比较分析。
Secondly, using locally variance estimation, a locally adaptive image-denoising algorithm was presented. Also this algorithm could be applied to the complex wavelet domain.
基于马尔科夫模型的浏览路径预测，仅仅从用户的浏览会话本身出发来预测用户下一步的链接，并不能捕获用户的真正兴趣所在。
Research prediction of based on Latent Markov model, only from to research conversation itself set out to predict user's next chaining user, can not catch users' real interest.
接着对网络流量模型算法分析，简单介绍了泊松模型，马尔科夫模型，AR,MA,ARMA模型，重点分析了ARIMA模型算法。
Then algorithm analysis of network traffic model, a brief introduction of the Poisson model, Markov model, ar, MA, ARMA model, focused on analyzing ARIMA model algorithm.
韵律短语的长度约束模型是利用隐马尔科夫模型对语句中韵律短语的长度规划进行建模，这个模型对短语的长度分布及韵律词与韵律短语的关系进行了描述。
And an HMM is used to model the phrase-length constraints, which include the distributions of the prosodic phrase lengths and the prosodic word number in the prosodic phrase.
提出了一种基于马尔科夫随机场模型的缺陷检测方法。
A method of vane defects detecting based on Markov random field is presented in this paper.
以钾离子单通道门控机理的分析为前提，探索钾离子通道门控动力学马尔科夫过程的模型，并选出相对简单的模型。
Taking the study of gating mechanism of potassium channel as the basis, this article approaches modeling method of Markov process of gating mechanism, and selects the relatively simple model.
特别地，约化模型应用了随机过程中的马尔科夫理论。
Particularly, the markov theory of random process has been used in reduced model.
分别采用二元决策图及马尔科夫方法对关键设备的动态故障树模型中静态子树和动态子树进行分析。
Binary Decision Diagram and Markov method are applied in the DFT modeling of satellite key devices to process static subtree and dynamic subtree, respectively.
然后给出了隐马尔科夫过程动态系统的一般模型。
Also we present commonly model of Hidden Markov Processes dynamic system.
由于强化学习理论的限制，在多智能体系统中马尔科夫过程模型不再适用，因此不能把强化学习直接用于多智能体的协作学习问题。
Due to the theoretical limitation that it assumes that an environment is Markovian, traditional reinforcement learning algorithms cannot be applied directly to multi-agent system.
马尔科夫过程模型已经广泛地应用于系统可靠性评价中。
Markov Process Model has been widely used in system reliability evaluation.
本文应用马尔科夫随机模型估计从正常到EB病毒抗体阳性，再到鼻咽癌的转移概率。
Markov stochastic chain model was used to analyze the transition rates from normal to positive anti EBV and from positive anti EBV to NPC.
此外，结果表明不同到期日利率期限结构可由缩压的马尔科夫区制转移CKLS模型获得。
Additionally, the results show that the term structure of interest rates of different maturities can be obtained with the nested Markov regime switching CKLS model.
该模型的核心部分是根据观测到的资料，通过蒙特卡洛马尔科夫链随机抽样的方法来估计变点位置的后验概率分布。
Given the observed hydrological data, the model can estimate the posterior probability distribution of each location of change-point by using the Monte Carlo Markov Chain (MCMC) sampling method.
在本论文中，我们讨论了三种视频源模型：自回归模型、马尔科夫调制流体流模型、马尔科夫调制泊松过程模型(MMPP)。
In this paper, we discuss three types of video source models: a autoregressive model, a Markov-modulated fluid flow model, and a Markov-modulated Poisson process model (MMPP).
在本论文中，我们讨论了三种视频源模型：自回归模型、马尔科夫调制流体流模型、马尔科夫调制泊松过程模型(MMPP)。
In this paper, we discuss three types of video source models: a autoregressive model, a Markov-modulated fluid flow model, and a Markov-modulated Poisson process model (MMPP).
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