Description: 语音端点检测算法，在matlab里面实现语音端点的准确检测。为读者开发噪声环境下的精确VAD提供思路。-voice endpoint detection algorithm in Matlab voice inside the precise endpoint detection. Readers development under noisy environments to provide accurate VAD ideas. Platform: |
Size: 1296 |
Author:王雷 |
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Description: 语音端点检测算法，在matlab里面实现语音端点的准确检测。为读者开发噪声环境下的精确VAD提供思路。-voice endpoint detection algorithm in Matlab voice inside the precise endpoint detection. Readers development under noisy environments to provide accurate VAD ideas. Platform: |
Size: 1024 |
Author:王雷 |
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Description: 端点检测算法采用现阶段比较精确的时频方差检测法，模式识别和模板匹配采用的是改进后的DTW算法（主要是限定了DTW的搜索路径，进一步精确了DTW的平行四边形的形状，进一步的减少匹配中的参数存贮量以及多余的搜索路径）。-Endpoint detection algorithm uses more precise at this stage variance time-frequency detection, pattern recognition and template matching is based on the improved DTW algorithm (DTW is mainly limited to the search path, and further precision of the DTW parallelogram shape, and further reduction of matching the parameters in the storage volume as well as the extra search path). Platform: |
Size: 7168 |
Author:李兰菊 |
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Description: 语音端点检测算法，在matlab里面实现语音端点的准确检测。为读者开发噪声环境下的精确VAD提供思路.-voice endpoint detection algorithm in Matlab voice inside the precise endpoint detection. Readers development under noisy environments to provide accurate VAD ideas. Platform: |
Size: 1024 |
Author:杨锐雄 |
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Description: 几篇带噪声语音信号端点检测算法的论文，希望对大家有用-Speech signal with noise several endpoint detection algorithm for papers, in the hope that useful Platform: |
Size: 1235968 |
Author:毋桂萍 |
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Description: dtw文件是运用DTW算法实现安静环境下语音识别的。其中vad.m是端点检测程序；lpc.m是计算LPC参数的程序；lpc21lpcc.m是计算LPCC参数的程序；mfcc.m是计算MFCC参数的程序；dtw.m是实现经典DTW算法的程序；dtw2.m是实现高效DTW算法的程序，testdtw.m是最终测试程序，其中可以通过改变其中的特征参数名选择不同的特征参数。-dtw file DTW algorithm is to use speech recognition in quiet environments. Which is the endpoint detection process vad.m lpc.m is to calculate the LPC parameters of the program lpc21lpcc.m procedure is to calculate the LPCC parameters mfcc.m procedure is to calculate the MFCC parameters dtw.m the classic DTW algorithm to achieve the program dtw2.m DTW algorithm is to achieve efficient procedures, testdtw.m is the final test program, which can change the parameters in which different parameters were selected. Platform: |
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Author:于军 |
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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: |
Size: 538624 |
Author:于军 |
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Description: 语音增强是影响语音识别系统性能的重要成分。为了比较语音增强算法的性能，采用Matlab软件进行了数值仿真，对不同噪声环境下的语音用3种不同的方法进行降噪，采用信噪比、端点检测等方法来降噪效果，并对几种增强算法的性能进行了比较分析。结果表明，在变噪声环境下短时谱MMSE法最佳，谱减法和维纳滤波法各有优点。-Speech enhancement of voice recognition is an important component of system performance. In order to compare the performance of speech enhancement algorithm using the Matlab software, a numerical simulation, speech under different noise environments with 3 different methods of noise reduction, the use of signal to noise ratio, endpoint detection method to the noise reduction effect, and a few kinds of enhanced performance of the algorithm were compared. The results show that changing the noise environment in the MMSE method was the best short-term spectrum, spectral subtraction and Wiener filtering methods have their advantages. Platform: |
Size: 376832 |
Author:static |
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Description: 为提高语音端点检测(VAD)在较低信噪比(10 dB)下的准确率,提出一种基于短时分形维数的改进算法。结合语音信号的特点,对2种常用的语音信号分形维数计算方法进行了比较和选择,同时采用动态跟随门限值实现语音端点的自适应检测。试验结果表明:对于信噪比6~10 dB的带噪语音,此方法可以实现整段语音的检测,而且具有一定的噪声鲁棒性,系统运行期间能够自适应调整门限值以适应环境噪声的变化,提高了VAD算法的准确率。这个是源码matlab。-In order to improve voice activity detection (VAD) in low SNR (10 dB) accuracy under proposed based on short-time fractal dimension of the improved algorithm. Combined with the characteristics of the speech signal, to 2 commonly used fractal dimension of speech signals are compared and calculated choice to follow the same dynamic endpoint threshold adaptive detection of voice. The results showed that: 6 ~ 10 dB for the signal to noise ratio of noisy speech, this method can detect the entire speech, but has some noise robustness, the system can be adaptively adjusted during operation to adapt to environmental noise threshold of changes to improve the accuracy of VAD algorithms. This is the source matlab. Platform: |
Size: 79872 |
Author:liuhongfu |
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Description: 介绍了一种基于 MA T L A B的多个特定人连接词语音识别的方法，并提出了在进行端点检测时，引入平均的概念能进一
步提高识别率。 此设计是以 L P C C系数、 D T W 算法为核心的基于图形界面的设计。 通过大量的实验测试 ，表明该方法基本达
到屏蔽外界环境的影响 ， 具有非常高的精度识别-: In this paper based on a number of specific persons MA TLAB conjunctions speech recognition method, and presented during endpoint detection, the introduction of the concept of the average recognition rate can be further improved. This design is LPCC coefficients, DTW algorithm as the core design based on graphical interface. Through a large number of experimental tests show that the method is basically to shield the external environment, with very high accuracy recognition Platform: |
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Author:薛莉 |
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Description: 介绍了一种基于MATLAB的多个特定人连接词语音识别的方法，并提出了在进行端点检测时，引入平均的概念能进一步提高识别率。此设计是以LPCC系数、DTW算法为核心的基于图形界面的设计。-A Based on MATLAB more specific conjunctions speech recognition, and during endpoint detection, the introduction of the concept of average to further improve the recognition rate. This design is based on LPCC coefficients, the DTW algorithm as the core-based graphical interface design. Platform: |
Size: 365568 |
Author:lhj |
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Description: 基于matlab的语音端点检测算法程序，该程序主要用于语音的起始位置和终止位置的检测-Speech endpoint detection algorithm based on matlab program, the program is mainly used in the detection of speech starting position and end position Platform: |
Size: 1024 |
Author:林玉梅 |
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Description: 是matlab环境中的一些算法，改进的语音端点检测算法，还有就是LMS算法，rls算法，与波束形成相结合的算法-Matlab environment in a number of algorithms, the improved speech endpoint detection algorithm, there is the LMS algorithm, RLS algorithm, combined with beamforming algorithm Platform: |
Size: 6144 |
Author:非鱼 |
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Description: 于市售 STM32 开发板上实现特定人语音识别处理项目。识别流程是：预滤波、ADC、分帧、端点检测、预加重、加窗、特征提取、特征匹配。端点检测(VAD)采用短时幅度和短时过零率相结合。检测出有效语音后，根据人耳听觉感知特性,计算每帧语音的 Mel 频率倒谱系数(MFCC)。然后采用动态时间弯折(DTW)算法与特征模板相匹配,最终输出识别结果。先用Matlab对上述算法进行仿真，经数次试验求得算法内所需各系数的最优值。而后将算法移植到 STM32 开发板上，移植过程中根据 STM32 上存储空间相对较小、计算能力也相对较弱的实际情况，对算法进行优化。最终完成于 STM32 微处理器上的特定人语音识别系统。-Implement speech recognition processing project in commercially available STM32 development board. Identification is the process: pre-filter, ADC, framing, endpoint detection, pre-emphasis, windowing, feature extraction, feature matching. Endpoint detection (VAD) short-time amplitude and short-term zero rate combined. After detecting an effective voice, according to the characteristics of human auditory perception, calculated for each frame of speech Mel Frequency Cepstral Coefficients (MFCC). Then dynamic time warping (DTW) algorithm and feature template matches the final output recognition result. First with Matlab simulation algorithm described above, after several trials to get the optimal value of each coefficient within the desired algorithm. The algorithm will migrate to STM32 development board, the porting process according to the STM32 relatively small storage space, computing power is relatively weak situation of the optimization algorithm. Finally completed on the STM32 micr Platform: |
Size: 325632 |
Author:Chenkly |
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Description: 摘 要：本论文主要研究了语音识别的基本原理,对语音识别系统的构成进行分析处理,其中包括预处理、特征参数提取、建立模块库、识别匹配几大部分。预处理又包括语音采样、预加重、加窗（汉明窗）、端点检测；特征提取的参数是梅尔频率倒谱系数MFCC。
该语音系统采用的是动态时间伸缩算法(DTW)，研究对象是特定人的语音识别，并在MATLAB平台上实现。为了进行后续研究，首先使用电脑中的录音系统录制了阿拉伯数字0—9的语音文件，并转化成 “.wav”格式的文件。-Abstract: This thesis mainly studied the basic principle of speech recognition, to analyze the composition of the speech recognition system, including the preprocessing, feature extraction, to set up the module library, identify several most matches. Pretreatment, including speech sampling, pre-emphasis, add window (hamming window), endpoint detection Feature extraction of MFCC MEL frequency cepstrum coefficient.
The voice system USES a dynamic time scale (DTW) algorithm, the research object is the speaker-dependent speech recognition, and realized in MATLAB platform.To carry out the follow-up study, the first to use the recording in a computer system to record the audio files of Arabic Numbers 0-9, and translated into . Wav format file. Platform: |
Size: 9216 |
Author:silver teng |
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Description: 算法是在MATLAB环境下编译的，其实现的功能主要是采用小波包BARK域中子带方差法来实现端点检测，实验结果表明在高信噪比情况下效果显著-Algorithm is compiled in MATLAB environment, and its main function is implemented using wavelet packet BARK sub-band variance jurisdictions to implement endpoint detection, experimental results show that at high SNR significant effect Platform: |
Size: 3072 |
Author:王栋 |
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