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- This paper presents a hybrid model of Continuous Density Hidden Markov Model (CDHMM) and the Multi-Layer Perceptron (MLP). 本文提出了一种由连续隐马尔可夫模型（CDHMM）与多层感知器（MLP）构成的混合模型，并将该模型应用于语音孤立词识别。
- A new synthetic aperture radar (SAR) image filter method is proposed based on hidden Markov model (HMM). 在小波域隐Markov模型(HMM)的基础上提出一种新的合成孔径雷达(SAR)图像的滤波方法。
- This paper proposes a novel contour tracking algorithm based on Hidden Markov Model (HMM) and optic flow. 提出了一个新颖的基于隐马尔科夫模型与光流的轮廓线跟踪算法。
- In this thesis, we use both Hidden Markov Model (HMM) and Weight Array Model (WAM) to predict the splice sites. 本文基于隐Markov模型(HMM)和权重阵列模型(WAM)两种方法来预测剪接位点。
- It produces Mongolian translation from the single language material through use of dictionary-based model and Hidden Markov Model. 基于HMM模型的蒙古文生成方法采用词典驱动模型和HMM模型从单语料生成蒙古文译文。
- Second, we discuss the three base question of Hidden Markov Model, induce two new algorithms, named mend Baum-Welch and mend Viterbi. 其次，本文在研究了隐马尔可夫模型的基础上，对其三个基本问题进行了比较细致的论证，并引入改进Viterbi算法。
- The continuous density hidden Markov model(CDHMM) is adopted, Viterbi and Baum-Welch reestimation algorithms is utilized to train and recognize the speech signals. 采用连续HMM模型，利用Baum-Welth重估、Viterbi算法进行训练和识别，实现系统软件设计。
- This paper presents a Chinese named entity recognition system that integrates the Hidden Markov Model (HMM) and rules which are automatic extracted from the training corpus. 本文实现的中文命名实体识别系统采用了隐马尔可夫模型(Hidden Markov Model,HMM)与自动规则提取相结合的方法。
- Then sub-state maximum likelihood and combining transition sub-state maximum likelihood (CTSSML) for parellel sub-state hidden Markov model are also presented. 在此基础上，提出了两种用于平行子状态隐马尔可夫模型的识别解码策略-子状态最大似然解码和联合转移子状态最大似然解码。
- The substance of this magisterial thesis is the research and improvement of speaker recognition which is based on the VQ (Vector Quantization) and HMM (Hidden Markov Model). 本论文主要内容是基于矢量量化(VQ) 和隐马尔可夫模型（HMM）的说话人识别算法的研究和改进。
- Proposes a hybrid approach for phoneme recognition based on combination of improved counter-propagation (CP) neural network and hidden Markov model (HMM). 提出了一种基于改进对偶传播 (CP) 神经网络与隐马尔可夫模型 (HMM) 相结合的混合音素识别方法。
- Through the combination of Hidden Markov Model POS tagging and the smoothing algorithm, we obtain a tagging precision of 86%, and a disambiguation of 82%. 在实现基于隐马尔可夫模型的词性标注同时，结合平滑算法，标注正确率达到86%25，排歧正确率达到82%25；
- By hidden Markov model, it combined with a prior segmentation model which is independent to noise feature as the compensation for the mismatch of acoustic model to enhance the robust performance. 然后,假设音节长度序列符合一阶马尔科夫过程,经过归一化处理后,求出了切分的先验概率公式,得到了贝叶斯方法的切分模型。
- Caption RecognitionFeature extraction using wavelet transformation and the combination of statistical language model and Hidden Markov Model methods finally achieved the identification of caption. 基于统计机器学习的字幕识别提取小波变换的特征并使用隐马尔可夫模型和统计语言模型的识别技术相结合的机器学习方法,实现字幕文字的识别。
- Hidden Markov Models are introduced to the area of ship-radiated noise recognition. 将隐马尔可夫模型引入到舰船噪声目标识别中。
- In order to use duration information in Language IDentification (LID) efficiently, the inhomogeneous Hidden Markov Model (HMM) with general topological structure is proposed, and is used to identify the language between Mandarin and English also. 为了充分利用语音信号中的段长信息,该文提出了一种具有一般拓扑结构的非齐次隐含Markov模型(Hidden Markov Model,HMM),并将其应用于中、英文语种辨识(Language IDentification,LID)系统。
- The whole process was divided into two steps.First to use the hidden Markov model for part-of-speech tagging, and then made use of match rules to amend and convert the result of the HMM step. 整个识别过程主要分成两个步骤，首先使用隐马尔可夫模型进行词性标注，然后利用具有优先级别的匹配规则对第一步的结果进行修正和转换。
- The discrete hidden markov model (DHMM) is adopted, Baum-Welth reestimation algorithm, forward-backward procedure and viterbi algorithm is utilized to train and recognize the speech signal. 语音识别模型采用离散隐马尔可夫模型(DHMM)，利用Baum-Welth重估算法、前向后向算法、viterbi算法来完成语音模板的训练和语音识别的任务。
- In this paper a method of continuous speech recognition based on hidden Markov models (HMM) and vector quantization (VQ) is discussed. 本文讨论基于隐马尔可夫模型(HMM)和矢量量化(VQ)的连续语音识别方法。
- Jeff A. Bilmes, “A Gentle Tutorial of EM Algorithm and its Application Parameter Estimation for Gaussian Mixture and Hidden Markov Models”. 郑顺德;“不特定语句中量语者辨识系统之设计研究;”国立中山大学电机工程研究所硕士论文;民国91年9月13日.
