| HMM识别孤立词的研究与实现 |
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| 引用本文： | 丁纪凯.HMM识别孤立词的研究与实现[J].东华大学学报(自然科学版),1990(3). |
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| 作者姓名： | 丁纪凯 |
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| 作者单位： | 中国纺织大学自动化及计算机系计算机控制学科组 |
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| 摘 要： | 本文介绍了隐式 Markov 模型(简称 HMM)识别语音的基本原理,对在训练孤立词模型过程中采用的 Baum-Welch 算法和 Viterbi 算法进行了研究,导出了参数估计的整套算式,提出了解决 HMM 在计算机上实现时出现的问题的方法及其实现算式。作者将 HMM 应用于汉语数字的识别,进行了不同算法的比较和不同初值条件的试验,给出了相应的识别结果。
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| 关 键 词： | 统计模型 语音识别 高斯分布 决策准则 参数估计 孤立词 实现 识别性能 |
STUDY AND IMPLEMENTATION OF ISOLATED WORD RECOGNITION USING HMM |
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| Ding Jikai.STUDY AND IMPLEMENTATION OF ISOLATED WORD RECOGNITION USING HMM[J].Journal of Donghua University,1990(3). |
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| Authors: | Ding Jikai |
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| Institution: | Department of Automation and Computer Science |
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| Abstract: | This paper describes the general principle of Hidden Markov Model for isolated wordrecognition,and also gives a series of formulas estimating parameters in the training ofMarkov model for isolated word,based upon the discussion of Baum-welch algorithm andViterbi algorithm.In accordance with the problems in the implementation of HMM,themethods of solution are proposed.HMM is successfully applied to Chinese digit recognition,and experimental results of thecomparison between two algorithms and different initial values are presented in the paper. |
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| Keywords: | statistical model speech recognition Gaussian distribution decision rules parameter estimation isolated word implementation recognition performance |
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