hidden markov model 中文意思是什麼
hidden markov model
解釋
隱馬爾可夫模型-
Back - propagation approach to discriminative training of hidden markov model
隱馬爾可夫模型的一種有區分力的反向傳播訓練方法 -
This thesis tries to update the cmdsr system to achieve the characters below : real - time, better robust, higher recognition rate, non - special - man. considering the disadvantages of traditional improved spectrum subtraction speech enhancement, this thesis proposes the theory of fuzzy spectrum subtraction based on the fuzzy theory and improved spectrum subtraction speech enhancement ; as for the difficulties of detecting the endpoint of speech signal, the thesis gives the table of initial and the improved parameters, with which we can confirm the endpoints of mandarin digit speech ; the thesis puts forward two - level digit real - time speech recognition system, the first level is based on discrete hidden markov model which is linear predictive coding cepstrum ( lpcc ) and difference linear predictive coding cepstrum ( dlpcc ), the second level is based on formant parameters ; as for the realization of hardware, the thesis depicts the realization of every part of cmdsr based on the tms320vc5402 in detail ; as for the development of software, the thesis gives the software design flow chart of cmdsr, simulates the basic theory with matlab language and gives the simulation results
針對傳統的「改進譜相減法語音增強」參數設定單一、環境適應能力差的缺點，提出了一種利用模糊理論和「改進的譜相減法」結合的「模糊譜相減法語音增強」 ;針對語音信號端點檢測困難的特點，通過matlab模擬試驗，給出了能夠準確確定數碼語音端點的初始和改進參數表;提出了利用基於線性預測編碼倒譜參數和差分線性預測編碼倒譜參數相結合的離散隱含馬爾可夫模型進行第一級識別、利用共振峰參數進行第二級識別的兩級漢語數碼語音識別系統，在保證系統實時性的同時，實現連接漢語數碼語音識別系統識別率的提高;在硬體實現上，詳細闡述了基於tms320vc5402的連接漢語數碼語音識別系統各部分硬體設計;在軟體開發上，給出了連接漢語數碼語音識別的軟體設計各部分的流程圖，並對各部分進行了matlab模擬，並給出了模擬結果。 -
Segmental training scheme for embedded hidden markov model
模型的分段訓練方法 -
Experimental results show that the cascading of the speech enhancer and a hidden markov model ( hmm ) based speech recognizer can significantly improve recognition accuracy in noisy environments without performance degradation for clean speech
通過3種不同的增強演算法用於純凈語音和3種類型帶噪語音的實驗結果分析比較表明，這一方法對純凈語音的識別精度幾乎沒有任何改變而大大提高了系統的抗噪聲性能。 -
In this thesis, an algorithm based on multiple features for recognition of escherichia coli promoter was proposed. firstly, word frequency method was utilized to extract the content ’ s information of a given sequence, and position weight matrix and hidden markov model were applied to analyze the information on structure, and then this information was input into a classifier
本文提出了一種基於多特徵的大腸桿菌啟動子判別演算法，即通過詞頻分析獲得序列的組成特徵，利用位置權重矩陣（ pwm ）和隱馬爾科夫模型（ hmm ）獲得序列的結構特徵，然後輸入到一個分類器中進行分類。 -
Caption recognition feature extraction using wavelet transformation and the combination of statistical language model and hidden markov model methods finally achieved the identification of caption
基於統計機器學習的字幕識別提取小波變換的特徵並使用隱馬爾可夫模型和統計語言模型的識別技術相結合的機器學習方法，實現字幕文字的識別。 -
Furthermore, a new elastic matching algorithm is designed with the combination of shape blending, which is based on physical elastic model, and a distinct improvement of performance is achieved. chapter 4 is mainly focused on recognition approaches using hidden markov model. firstly, the general concepts and algorithms of hidden markov model are described, and then, a new model called ddbhmm is discussed and compared with the classic model in detail
文中首先介紹了一種自適應形態校正技術，隨后討論了彈性匹配中的一些基本演算法及存在的問題，並在此基礎上研究了一種新的彈性匹配演算法，其主要特點是在匹配演算法中引入了一種基於物理模型的形變度量，能夠有效地改善原有演算法的性能。 -
In the thesis, we select the mel - frequency cepstrum coefficients based on analyzing a lot of parameters of speech signal. mel cepstrum is of better recognition and anti - noise capability. ( 2 ) dynamic time warping, vector quantization, hidden markov model and artificial neural network can be used in speaker recognition
（ 2 ）現有的說話人識別方法有動態時間規整法、矢量量化法、隱馬爾可夫模型和神經網路法等，其中hmm已成為目前最佳的說話人識別處理模型。 -
The uyghur pos tagging is studied by applying the probabilistic method and the unigram hidden markov model ( hmm ) is adopted
本文採用了一階隱馬爾可夫模型，並且通過rft相對概率訓練獲得了模型參數。 -
As a statistics model, hidden markov model ( hmm ) have been widely used in pattern recognition and stochastic signal processing
隱馬爾科夫模型（ hiddenmarkovmodel ，簡記為hmm ）作為一種統計模型，在模式識別與隨機信號處理中有著廣泛的應用。 -
This paper studies 3 kinds of algorithms : the viterbi algorithm, multiresolutional algorithm based on wavelet transformation and bayesian bootstrap algorithm. the viterbi algorithm is based on the hidden markov model theory and it is a kind of map estimation, this paper studies this algorithm and puts up an algorithm that suits for filtering in the presence of interference. multiresolutional algorithm takes full advantage of multiresolutional data, we can see it has a better filtering ability than the traditional filtering methods ; bootstrap algorithm is a recursive bayesian estimation, it describes the probability density function by the samples, so it can be used to nonlinear non - gaussion filtering, the simulation result of the two groundings is presented
Viterbi演算法以隱馬爾可夫理論為基礎，是一種最大后驗概率估計方法，本文對該演算法進行了研究，給出了一種適合於非高斯干擾條件下的濾波方法；多分辨分析方法充分利用到了多解析度測量數據所包含的信息，從模擬結果中可以看出，該方法的濾波精度要高於傳統的濾波演算法；自主濾波方法是一種遞推貝葉斯估計演算法，它利用采樣點來描述目標狀態的概率密度函數，因而適用於非線性、非高斯條件下的濾波，本文分別對這兩種情況下的濾波進行了模擬。 -
Wlththe fleetingprogress ofcomputerteccioanddlgltaltechology ， newly devefoped duntlzed medicine techniques go on expending ， while computed radlographyls atyplcalmethods itheprocedure ofcomputedradlographylmaging ， we should irstlygetacross the characters ofklnds ofnolses andthe relationship betweenthe image signals andnolses basedonthe specialties o 土 computedradlo graphy images and medical image processing ， we h 。 e study the filtering methods r competed radlography images noises our mmor wo indudes ： 1 on the base of analyzing computed radlography imaging system in detail ， the author th 讓出 that the mnyor … noises are gausslanwie noise and polsson noise then ， the dlfferemrelationshlp ofbe 加 eentwo kinds ofnolses and sipal were studied completely 21 by conslderingboththe charactenstlcs ofcomputed radlography images andthe statistical feres ofwavelettransformed images ， a multlscale image iltenng algonthln ， which based on wo state hidden markov model （ hmm ） and mlxtule gausslan statistical model ， has been used to decrease the gausslan white noise in compmed images we expenmems as well as the comparison ith other denolsing methods werepresentedatlast ，
在cr成像過程中，不可避免地引入各種噪聲干擾，極大地影響了醫生診斷的準確性，為此，只有弄清干擾圖像信息的各種噪聲的來源、特徵及其與信號的相互關系，才能有效地將之消除。結合cr影像以及醫學圖像處理的特點，本文研究了cr影像噪聲濾除的方法。主要工作有： [ 1 ]在詳細地分析cr成像系統的基礎上，指出固有噪聲和泊松噪聲是影響成像質量的兩種主要噪聲，並深入地討論了這兩種噪聲與圖像信號之間的相互關系。 -
This paper first introduces the aspects of network performance research based on traffic measurement, modeling and analysis and its state - of - the - art, secondly summarizes then the concept, models and analysis tools of self - similar traffic, and analyzes scaling behavior of packet loss with self - similar traffic input by wavelets method, thirdly introduces hidden markov model and its applications on network performance research, and then explores the cross - traffic inferring technology and the disadvantages of existing methods. after that the paper develops a new method for cross - traffic inferring based on delay jitter measurement, proves its correctness by experiments, and applies it to self - similar traffic background and real traffic trace to investigate its availability,
本文首先闡述了基於流量測量與分析的網路性能研究方向和研究現狀，而後介紹了自相似流量的基本概念和相關建模和分析技術，並採用小波分析的方法分析了單路復用網路模型在自相似流量下丟包的尺度特性，其次介紹了隱馬爾可夫模型以及其在網路性能研究中的應用，最後在此基礎上考察了網路流量推斷技術，分析了現有的方法的不足之處，提出了一種新的基於探測流延遲抖動測量的流量推斷測量技術，通過實驗證明了該方法的正確性，然後將其應用到自相似流量背景下考察了其對自相似性的推斷刻畫能力，並且通過實際流量檢驗了其有效性。 -
A hidden markov model based on factor analysis
基於因子分析的隱馬爾可夫模型 -
Firstly, we accurately locate the pupils of eyes in the face image according to the proportion relationship of face features and gray information. then we normalize the rotation, scale and grayscale of face image. we recognize human face using the method based on embedded hidden markov model ( ehmm ) that used the 2d - dct coefficients as the observation feature
其次在對人臉檢測和識別技術研究中，詳細介紹了人臉檢測技術的研究現狀，在使用viola提出的基於haar - like特徵的人臉檢測方法進行自動人臉檢測之後，提出了一種基於人眼定位的有效人臉圖像歸一化演算法，可以準確檢測人眼瞳孔位置，並在此基礎上對人臉圖像作旋轉、尺度和灰度的歸一化校正，並且用基於2d - dct特徵提取和ehmm人臉識別方法作人臉的分類識別進行了試驗。 -
The continuous density hidden markov model ( cdhmm ) is adopted, viterbi and baum - welch reestimation algorithms is utilized to train and recognize the speech signals
採用連續hmm模型，利用baum - welth重估、 viterbi演算法進行訓練和識別，實現系統軟體設計。 -
Recent research has demonstrated the strong performance of hidden markov model applied in information extraction. however, the information extraction based on hidden markov model generally takes a token as a basic extraction unit, and the information of format and list separators is not taken into account. based on the natural structure of text, a block - based hidden markov model is provided
目前基於隱馬爾科夫（ hmm ）信息抽取模型一般以單詞作為基本抽取單位，考慮到文本排版格式、分隔符等信息的存在，文本實際上可以看作是由一些文本分塊序列組成，同一分塊內的所有單詞只可能屬于同一個狀態，而不同分塊可以屬於一個或多個狀態。 -
A baseline system of keyword spotting based on continue hidden markov model ( chmm ) is constructed
研究的主要內容包括: 1 .基於連續隱馬爾可夫模型（ chmm ）框架的非特定人關鍵詞識別基線系統的構建。 -
In this paper, an adaptive hidden markov model ( ahmm ) approach for on - line hand - drawn shape recognition is presented. in our method, hmms are chosen as the core recognizer due to its great ability to model stochastic time series. many improvements are made to the traditional hmm recognizer in order to increase the flexibility of the recognition system, the resulting framework can not only adaptively learn from training data, but also can adapt system behavior to input hand - drawn shape
本文提出了一種用於聯機手繪圖形的自適應隱馬爾科夫模型識別方法，該方法利用隱馬爾可夫模型（ hmm ）對時序隨機序列的描述能力作為手繪圖形識別中的核心分類器，並且在傳統hmm識別結構的基礎上進行了改進，使得識別系統不僅具有自適應學習訓練樣本的能力，而且具有根據輸入圖形特徵調節系統行為的能力。 -
In this thesis, we firstly give a brief introduction of the speech recognition processes and its realization method, especially of the basic theory, topology structures and algorithms of hidden markov model
首先介紹了語音識別過程的各個環節及其實現方法，重點介紹了hmm的基本理論、拓撲結構和演算法。
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