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CVadMfccDtw
- 一个基于DTW的的孤立词的语音识别系统，采用率为800，可以识别六个字左右的短语，是特定人的识别系统。运行时请更改工作目录：工程->设置->调试->更改工作目录即可
DTWforspeechregon
- 一种改进的DTW算法，能有较好的处理效率
00101102
- 介绍ar，和dtw两种算法，包含一个实际应用的例子-introduced ar, and two algorithms dtw, including a practical application examples
DTWSolution
- 语音识别算法的DTW识别程序，VC 功能用于语音识别-DTW distinguish program using audio distinguish arithmetic.it could be used to distinguish audio.
vc在线签名鉴定原码
- vc在线签名鉴定原码,包含AR算法和DTW算法及应用实例.-vc online signature verification original yards, including AR algorithm and DTW and application examples.
基于MATLAB的语音识别系统程序包括HMMDTWRecord三个matlab的M文件
- 基于MATLAB的语音识别系统程序,包括HMM,DTW,Record三个matlab的M文件
dtw算法应用实例
- c程序
语音识别中的DTW算法
- 语音识别系统中的经典算法，DTWDynamic Time Warping，动态时间归整）算法，该算法基于动态规划（DP）的思想，解决了发音长短不一的模板匹配问题，用于孤立词识别，本程序可以独立运行。
几种语音识别算法的比较
- 几种小训练样品集的数字语音识别模型的比较性研究
基于DTW的语音识别
- 程序
kinect gesture
- 基于DTW的kinect手势识别程序 有GUI
dtwvc.rar
- 用vc编写的dtw算法，调试通过，用于手写签名识别的,Vc prepared using DTW algorithm, debug is passed, for the handwritten signature recognition
endpoint
- 这是一段关于语音处理与识别的程序，包括预处理，端点检测，线性倒谱系数求解，并运用dtw算法进行模式匹配。-This is a speech processing and recognition on the program, including pretreatment, endpoint detection, linear cepstrum solution, and the use of dtw algorithm for pattern matching.
testdtw(zhu)
- function dist = dtw(t,r) n = size(t,1) m = size(r,1) 帧匹配距离矩阵 d = zeros(n,m) for i = 1:n for j = 1:m d(i,j) = sum((t(i,:)-r(j,:)).^2) end 累积距离矩阵 D = ones(n,m) * realmax D(1,1) = d(1,1) 动态规划 for i = 2:n
MFCCdeC
- 工程包括声音文件的读取、预处理、MFCC参数的提取、最后的聚类函数，对于做语音识别的人帮助很大-The works will include the sound files to read, pre-treatment, MFCC parameters extracted, the final clustering function, to do speech recognition for the great help of people
dtw_speech_reco
- 基于DTW的孤立词语音识别，具有很高的识别率，代码为c语言-speech recognition Using DTW
DTW(matlab)
- matlab编写的时间序列动态弯曲算法，包含有使用说明-If you pass in 2 vectors it returns the unnormalized distance between the vectors, the accumulated distance between them, the length of the warping path (the normalizing factor), and the warping path points.
LCS-VC60
- C语言DTW算法实现,主要功能: 1)快速近邻DTW比较 2)算法稳定 3)节省内存-== Key features == 1) Fast Dynamic Time Warping nearest neighbor cost retrieval. 2) Persistence 3) External-memory: you need only a constant amount of RAM
onlineqm
- 在线签名鉴定，用AR实现算法和DTW算法实现，还包括一个比较综合的实例，对此方面研究的朋友有用处-Online signature identification, use AR to achieve algorithm and DTW algorithm, but also a more comprehensive example of this research useful friends