Description: ：用C 语言实现了一个用于控制家电开关的声音模块. 该声音模块采用当前语音识别系统的主流技
术——隐马尔可夫模型（HMM）技术，以及线性预测倒谱计算和矢量量化技术. 命令（单词）的正确识别率
在97 以上. 介绍了声音模块的设计方案，并就实现该声音模块的过程中所遇到的具体问题进行了讨论.-: The C language realization of a switch used to control the voice module appliances. The sound module voice recognition systems using current mainstream technologies- Hidden Markov Model (HMM) technology, as well as the linear prediction cepstrum calculation and vector quantization technology. command (word) the correct recognition rate in more than 97 . introduced a sound module design and the realization of the sound module on the course of the specific problems encountered were discussed. Platform: |
Size: 200704 |
Author:刘文 |
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Description: Hidden Markov Models (HMM) have been used with
some success in recognizing printed Arabic words. In this
paper, a complete scheme for totally unconstrained
Arabic handwritten word recognition based on a Model
discriminant HMM is presented. A complete system able
to classify Arabic-Handwritten words of one hundred
different writers is proposed and discussed. The system
first attempts to remove Platform: |
Size: 52224 |
Author:ammar |
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Description: 基于隐马尔可夫模型的语音单字识别研究：本文针对线性模型在语音识别中的不足, 进行了隐马尔可夫模型(HMM)在
语音单字识别中的研究,主要对观察输出概率求解、 最佳状态序列寻找、 参数估计和
模型参数的选择进行了探讨.-Based on hidden Markov model speech word recognition: the lack of the linear model in speech recognition, hidden Markov model (HMM) speech word recognition, mainly on the observation of the output probability for solving the most best state sequence search, the choice of the parameter estimation and model parameters are discussed. Platform: |
Size: 198656 |
Author:郭粉玉 |
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Description: 一种基于隐马尔科夫模型的孤立词的的语音识别实验，可以试验0到9的数字语音识别。-An isolated word speech recognition experiment based on the hidden Markov model, can test 0 to 9 digit speech recognition. Platform: |
Size: 606208 |
Author:裴安山 |
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Description: The speech signal for the particular isolated word can be viewed as the one generated using the sequential generating probabilistic model known as hidden Markov
model (HMM). Consider there are n states in the HMM. The particular isolated
speech signal is divided into finite number of frames. Every frame of the speech
signal is assumed to be generated from any one of the n states. Each state is modeled as the multivariate Gaussian density function with the specified mean vector
and the covariance matrix. Let the speech segment for the particular isolated word
is represented as vector S. The vector S is divided into finite number of frames
(say M). The i th frame is represented as Si . Every frame is generated by any of the n
states with the specified probability computed using the corresponding multivariate
Gaussian density model. Platform: |
Size: 787456 |
Author:Khan17
|
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Search - hidden markov model word recognition