中文说明：应用背景基于MATLAB平台，采用DTW模型实现了一个10位数的中文语音识别。此代码使用动态时间规整技术来识别语音信号并录制。关键技术动态时间规整算法（DTW）是用于测量两个时间序列之间的相似性的算法可能不同在时间或速度上。例如，行走模式的相似性被检测到使用DTW，即使一个人走路的速度比其他的，或如果有加速和减速过程中的观察。大田已适用于视频，音频和图形数据的时间序列—事实上，任何可以被转化成线性序列的数据都可以分析了DTW。一个众所周知的应用程序已经自动语音识别，以应付不同的讲话速度。其他应用程序包括说话人识别和在线签名识别
English Description：
Application background based on MATLAB platform, using DTW model to realize a 10 digit Chinese speech recognition. This code uses dynamic time warping technology to recognize speech signals and record them. The key technology dynamic time warping (DTW) algorithm is used to measure the similarity between two time series, which may be different in time or speed. For example, walking pattern similarity is detected using DTW, even if one person walks faster than the other, or if there are observations during acceleration and deceleration. OTA has been applied to time series of video, audio and graphic data - in fact, any data that can be converted into linear series can be analyzed by DTW. A well-known application has automated speech recognition to cope with different speech speeds. Other applications include speaker recognition and online signature recognition