搜索资源列表
hmm--matlab
- 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 :)
语音识别中求Mel倒谱系数的matlab程序
- 这是一个在语音识别中求Mel倒谱系数的matlab程序。 这个程序里面还需要用到几个其他的函数，都在VoiceBox工具箱里面，可以到网上下载,Calculate the mel cepstrum of a signal，The program also needs to be used in several other functions, in VoiceBox toolbox, can to download
WienerScalart96.rar
- 用Wiener滤波器进行的语音增强算法，效果还可以。 函数输出为WIENERSCALART96(signal,fs,IS),Using Wiener filter speech enhancement algorithm, the effect can be. Function output WIENERSCALART96 (signal, fs, IS)
endcheck.rar
- 一个完整的语音端点检测MATLAB程序，包含有完整的文件，通过调试，正确可用,A complete voice activity detection MATLAB procedures, contains the complete document, through commissioning, can be used correctly
dtw.rar
- 很有用的DTW音频识别算法，可执行，matlab实现。,A useful DTW audio recognition algorithm, writed by matlab. The code can be performed.
song
- 用matlab进行语音合成，用到matlab GUI编程，界面友好。本人课程设计作品，希望对各位有所帮助。-Using matlab for speech synthesis, using matlab GUI programming, user-friendly. I curriculum design works, you want to be helpful.
gmmem_several
- GMM是语音识别中比较成熟的算法之一，这个MATALB完整的实现了基于GMM算法的识别训练程序，可以适用于GMM识别和学习的同行的参考-GMM is a speech recognition algorithm for the more mature one, this MATALB full implementation of the identification algorithm based on GMM training procedures, can be applied to GMM
noisex
- 递归度信号处理.可用语语音信号处理,如端点检测,清浊音分割.-Recursive-degree signal processing. Can be language speech signal processing, such as endpoint detection, segmentation Voicing.
LMSyuyin
- 本文将现实中采集到的一段语音加上了高斯白噪声，利用了LMS算法，将噪声滤除，仿真效果良好-This article will be collected in reality a section of voice coupled with a Gaussian white noise, using the LMS algorithm, the noise filtering, simulation results
BEMD_compress
- 介绍HHT算法及其应用的论文，建议看看，-HHT algorithm and its application to introduce the paper, it is recommended to see,
matlab
- 语音识别MATLAB代码，希望有所帮助，大家自己看下吧有兴趣的话-Speech Recognition MATLAB code, want to be helpful, we are interested in their own right facie case
speeker
- 一个跟说话人识别相关的资料，非常适合入门，精彩不容错过呀-A speaker recognition with the relevant information is well suited to introduce the best of me not to be missed
lpcy
- 。LPC分析是进行语音分析的有效技术之一,它提供了1 组简洁的模型参数,这组参数能够比较精确地表征语音信号的频谱幅度, 而且获得这些参数所需的计算量相对较小-. LPC analysis is an effective voice analysis technologies, which provides a concise group of model parameters, which can be more precise set of parameters that characteri
coleawin_Matlab_Speech_Analysis
- 波形和频谱双显示 记录讲话直接进入MATLAB 手动分割讲话波形-创建标签文件 波形编辑-切割，复制或粘贴 共振峰分析-显示共振轨道的F1 ， F2和F3 基音分析 过滤工具-语音信号滤波器截止频率 比较工具-比较两个波形的频谱距离使用几种措施 增加噪声-Dual time-waveform and spectrogram displays Records speech directly into MATLAB NEW Displays time-a
voice-box
- 语音信号处理工具箱，提供了大量语音信号处理可能用到的MATLAB程序-Speech Signal Processing Toolbox.It provides a substantial voice signal processing that may be used in MATLAB program.
a_novel_speake_identification_system_based_on_GMM_
- 文中详细介绍了一种基于GMM 与 SVM的说话人识别系统。包括特征提取，算法实现及实验数据。对语音处理及说话人识别技术的研究者很有帮助。-Introduce a novel speake identification system based on GMM and SVM,with feature extract,algorithm research and experiment data.This doc would be helpful to those who are working on
endpointdetection
- 语音检测的目的是在一串连续的记录信号中将重要的信息分离出来。在电信应用领域，语音检测是必需的。对于自动语音识别，端点检测在分离重要语音信息时是必需的，这样可以产生语音模式或语音模板。-Voice detection is aimed at a string of consecutive records of information important signal will be separated. At the field of telecommunications applications
lpcc
- 本源码为语音识别中lpcc（线性预测倒谱系数）的计算方法。经试验，可用。为方便大家理解，关键语句和函数后面都加上了注释。-The source code for speech recognition in lpcc (Linear Prediction Cepstrum Coefficient) is calculated. After testing, can be used. To facilitate the understanding of everyone, the key behin
xiaobobianhuan
- 利用小波变换多尺度分析的特性,在小波域的各个尺度上选取不同的阈值对基本的频域谱减法进行改进,并且根据清浊音各自不同的特点,在去噪过程中加以分离,保留了语音中的清音成分,使语音更加饱满。-The use of multi-scale wavelet analysis of the characteristics of the individual in the wavelet domain scale threshold select a different frequency domain of
Speech Encoding - Frequency Analysis MATLAB
- 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 sig