GMM_SID

c#写的语音识别程序，识别效率还算可以。且根据识别可以给出相应的识别百分比。适合研究语音识别以及tts的参考！(c# to write the speech recognition process, identify efficiency figure. And recognition can be given according to the percentage of the corresponding identification. Tts speech recognition, as well as for research reference!)

GMM说话人识别平台全套，从特征提取到模式识别，到识别率，一整套都有(GMM speaker recognition platform full, from feature extraction to pattern recognition, the recognition rate, set both)

传统语音增强算法，谱减法和MCRC算法，matlab源代码。(Traditional speech enhancement algorithm, spectral subtraction and MCRC algorithm, matlab source code.)

本资料是自己编写的基于MATLAB语音工具箱的DTW动态时间规整的说话人识别，具有很高的识别率(This information is written in their own voice-based MATLAB toolbox DTW dynamic time warping speaker recognition with high recognition rate)

本实例程序是基于matlab的语音识别程序，通过提取传统的MFCC特征集，采用当前流行的SVM分类器，作为对比实验是一个不错的选择。(This example program is popular SVM classifier matlab speech recognition program to extract MFCC feature set, as a comparative experiment is a good choice. )

Chapter 2 Low Bit-Rate Speech Coding In this chapter an overview is given of speech coding techniques at several bit rates. Most of them use Linear Prediction. This overview is not meant to be complete its purpose is to make the reader somewhat familiar with Linear Predictive Coding which is necessary for a proper understanding of later chapters. Section 2.1 treats the subject of quantisation and coding. In section 2.2 a description of speech production and speech sounds is given. Coders based on linear prediction can be considered as being based on a simple speech production model. This model is explained in section 2.3. Section 2.4 describes various speech coding algorithms and techniques. Section 2.5 briefly describes some measures for the quality of coded speech.(In this chapter an overview is given of speech coding techniques at several bit rates. Most of them use Linear Prediction. This overview is not meant to be complete its purpose is to make the reader somewhat familiar with Linear Predictive Coding which is necessary for a proper understanding of later chapters. Section 2.1 treats the subject of quantisation and coding. In section 2.2 a description of speech production and speech sounds is given. Coders based on linear prediction can be considered as being based on a simple speech production model. This model is explained in section 2.3. Section 2.4 describes various speech coding algorithms and techniques. Section 2.5 briefly describes some measures for the quality of coded speech.)

语音信号去噪，在MATLAB开发环境下，对含噪声的语音信号进行卡尔曼滤波，实现语音增强的功能。(The voice signal denoising, the Kalman filter, the noisy speech signal in the MATLAB development environment Wave, speech enhancement.)

EVRC的语音编码算法研究及仿真,及二阶固定码本搜索算法(Speech Coding Algorithm and Simulation of EVRC)

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).)

matlab 实现的基于GMM的说话人识别系统(matlab implementation of the GMM-based speaker recognition system)