资源列表
speechfeaextra1
- Short introduction to speech feature extraction code: Step 1: Create speech features: If you have a one stereo file with each speaker in one channel use: features = speech_features_stereo( demo.wav ) If you are using several mo
VQ-GUI
- 基于矢量量化（ＶＱ）的说话人识别，只需输入训练文件夹，即可训练和识别。-Vector quantization (VQ) based speaker recognition, simply enter the training folder, to training and recognition.
Timit-wav
- 语音识别中有个著名的语音库TIMIT，其中的wav文件和我们平常的不一样，这个程序就是读取wav文件的-Speech recognition in a famous speech database TIMIT, one of the wav file and we usually do not like this program is to read wav files
dtw
- 语音识别中的DTW（Dynamic Time Warping，动态时间归整）算法的c语言实现。-Voice recognition of the DTW (Dynamic Time Warping, dynamic time of the whole) algorithm c language.
Voicebox
- 在用matlab做语音信号处理时经常用到的enframe等函数，这在matlab中是没有的，需要下载专门的工具箱，这是从官网上下载的，可直接解压安装。具体安装方法网上一搜就能搜到。-Frequently used in speech signal processing using matlab enframe function in matlab there is no need to download a special toolbox, which is downloadable from
bjjiinbaoma
- 一种基于自适应子带频谱熵的稳健性语音音端点检测，自己写的，希望对大家有帮助。 -A robustness of the voice tone endpoint detection based on adaptive sub-band spectral entropy, write your own, we hope to help.
Music
- 阵列麦克风中的music算法，用matlab仿真的-Music algorithm in array microphone, matlab simulation
melp
- melp 2.4K 浮点算法 语音编解码程序-melp 2.4K floating-point arithmetic voice encoding and decoding procedures
pcb
- RLS算法基本原理 里面有rls算法的基本原理和实现的过程-Rls algorithm basic principles and process in which the basic principle of the RLS algorithm
noise_cancelation
- 对语音信号中叠加上不同的噪声，用NLMS方法对带噪语音进行消噪处理。-Different noise superimposed on the speech signal, the NLMS method for Noisy Speech Denoising.
mfccPSVM
- 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.
MFCCPHMM
- 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HMM分类器，作为对比实验是一个不错的选择。-This example the program matlab speech recognition program to extract MFCC feature set, the HMM classifier, as a comparative experiment is a good choice.