Description: 用matlab编写的基于hmm模型的语音识别程序，但是调试好像有些问题，欢迎指正:)-prepared using Matlab model based hmm voice identification procedures, but there seems to be some debugging, and welcomes the correction :) Platform: |
Size: 329728 |
Author:dorothy |
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Description: 利用opencv裡的hmm function 來達到人臉識別的功能
-Using opencv in the Face Recognition hmm function to achieve the function of Platform: |
Size: 1024 |
Author:張乃文 |
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Description: 这是用java实现的隐式马尔科夫模型，可以应用在语音识别领域。-This is achieved using java Hidden Markov Model, can be applied to the field of speech recognition. Platform: |
Size: 29696 |
Author:van yee |
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Description: This code implements in C++ a basic left-right hidden Markov model
and corresponding Baum-Welch (ML) training algorithm. It is meant as
an example of the HMM algorithms described by L.Rabiner (1) and
others. Serious students are directed to the sources listed below for
a theoretical description of the algorithm. KF Lee (2) offers an
especially good tutorial of how to build a speech recognition system
using hidden Markov models. Platform: |
Size: 15360 |
Author:aaaaaaa |
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Description: Hidden_Markov_model_for_automatic_speech_recognition
This code implements in C++ a basic left-right hidden Markov model
and corresponding Baum-Welch (ML) training algorithm. It is meant as
an example of the HMM algorithms described by L.Rabiner (1) and
others. Serious students are directed to the sources listed below for
a theoretical description of the algorithm. KF Lee (2) offers an
especially good tutorial of how to build a speech recognition system
using hidden Markov models. Platform: |
Size: 23552 |
Author: |
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Description: 基于HMM的语音识别的一些资料，用matlab实现。希望对大家有帮助-HMM-based speech recognition of some of the information, using matlab realize. Hope everyone has to help Platform: |
Size: 202752 |
Author:黄卓芬 |
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Description: speech recognition using hidden markov model making it speaker independent
Using Matlab 7.5[2008b] Platform: |
Size: 10031104 |
Author:kani |
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Description: hmm文件时运用HMM算法实现噪声环境下语音识别的。其中vad.m是端点检测程序；mfcc.m是计算MFCC参数的程序；pdf.m函数是计算给定观察向量对该高斯概率密度函数的输出概率；mixture.m是计算观察向量对于某个HMM状态的输出概率，也就是观察向量对该状态的若干高斯混合元的输出概率的线性组合；getparam.m函数是计算前向概率、后向概率、标定系数等参数；viterbi.m是实现Viterbi算法；baum.m是实现Baum-Welch算法；inithmm.m是初始化参数；train.m是训练程序；main.m是训练程序的脚本文件；recog.m是识别程序。-hmm HMM algorithm file using speech recognition in noisy environments. Which is the endpoint detection process vad.m mfcc.m procedure is to calculate the MFCC parameters pdf.m function is calculated for a given observation vector of the Gaussian probability density function of output probability mixture.m is to calculate the observation vector for a HMM state output probability of observation vector is the number of Gaussian mixture per state output probability of the linear combination getparam.m before the calculation of the probability function, backward probability, calibration coefficients and other parameters viterbi.m is Viterbi algorithm implementation baum.m Baum-Welch algorithm to achieve inithmm.m is the initialization parameters train.m is the training program main.m training program is a script file recog.m is to identify procedures. Platform: |
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Author:于军 |
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Description: 在MATLAB 环境下利用语音工具箱Voice Box 实现基于连续概率密度隐含马尔科夫模型的汉语语音识别系统-Using Voice- Box a mandarin speech recognition system is realized based on CDHMM in MATLAB environment. Platform: |
Size: 210944 |
Author:江丰安迪 |
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Description: 语音识别的HMM模型，采用matlab，对新手很有帮助的-HMM model for speech recognition using matlab helpful to novice Platform: |
Size: 115712 |
Author:apple |
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Description: 在语音识别领域改代码运用隐马尔科夫模型可以实现很好的识别性能-Change the code in the field of speech recognition using Hidden Markov model can achieve good recognition performance Platform: |
Size: 10240 |
Author:张涛 |
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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 - SPEECH RECOGNITION USING HMM