中文说明：Dynamic Time Warping（DTW）诞生有一定的历史了（日本学者Itakura提出），它出现的目的也比较单纯，是一种衡量两个长度不同的时间序列的相似度的方法。应用也比较广，主要是在模板匹配中，比如说用在孤立词语音识别（识别两段语音是否表示同一个单词），手势识别，数据挖掘和信息检索等中。假定一个孤立字（词）语音识别系统，利用模板匹配法进行识别。这时一般是把整个单词作为识别单元。在训练阶段，用户将词汇表中的每一个单词说一遍，提取特征后作为一个模板，存入模板库。在识别阶段，对一个新来的需要识别的词，也同样提取特征，然后采用 请点击左侧文件开始预览 ！预览只提供20%的代码片段，完整代码需下载后查看 加载中 侵权举报
English Description：
Dynamic time warping (DTW) has a certain history (proposed by Itakura, a Japanese scholar), and its purpose is relatively simple. It is a method to measure the similarity of two time series with different length. It is also widely used, mainly in template matching, such as isolated word speech recognition (identifying whether two segments of speech represent the same word), gesture recognition, data mining and information retrieval. Assuming an isolated word speech recognition system, the template matching method is used for recognition. At this time, the whole word is generally regarded as the recognition unit. In the training phase, the user will say every word in the vocabulary once, extract features as a template and store them in the template library. In the recognition stage, for a new word that needs to be recognized, the feature is also extracted, and then it is used