该压缩包中包含一个完整的语音识别程序，代码使用matlab实现，使用了经典的GMM，HMM模型。附件中还包含完整的说明文档，介绍了一些基本原理，和该源码的使用方法。是语音识别入门必读源码之一。该源码只需要很小的改动就可以用来做说话人识别。 ：}

用于录音 ，适用于语言识别，说话人识别，录音程序，可直接调用

本资源是基于MATLAB的说话人识别系统，是基于HMM模型 的，具有很高的识别效率，大家可以放心使用

基于hmm说话人识别与确认系统 mfcc特征提取 Hmm extraction based speaker recognition and verification system features mfcc

Auto Speaker Recognition main.py the main file for test audio_record.py record audio from micro phone count_days.py count days between two date. 20110805 20160903 mfcc_feature.py extract mfcc feature from wav files SGD.model* the trained model on train set , and the accurate is 70% util.py contains the most useful functions train train data is 75% of all the data test test data is 25% of all the data and has no overlap with train set classification_SGD.py is the main classification function py file , and it used the sklearn's SGD niter was set 10000 could get 70% of accurate. classification_BNB.py this is the sklern naive_bayes BernoulliNB , and it reach to just 56% classification_DT.py this is the sklern tree.DecisionTreeClassifier , and it reach to just 63% classification_GB.py this is the sklern GradientBoostingClassifier, and it reach to the best now of 76% when n_estimators=1000, but it produce too many model components to store. classification_GNB.py this is the sklern naive_bayes GaussianNB, and it reach to just 63% vote_result.py add a vote decsion , every method have the acurrcy number ticiks to vote the final answer. after the vote , we achived 96% at test set. beta1.0

该说话人识别系统通过提取语音信号的MFCC特征参数，并由DWT算法计算得测试样本与各模板样本之间的距离，由最近临准则，完成说话人识别。优点是算法简单，无需大量样本，缺点为需保证说话人说话的内容一致。

课设找到的代码 并加了注释 写了学习文档和相关内容的扩充 对于入门来说应该是很有帮助的 感谢代码原来的提供者 然后希望这份文件可以被更多人使用 程序一直可用

本文详细介绍了用于说话人识别的GMM-UBM模型,包括处理流程和模型训练方法

三篇很好的论文，详细的介绍了声纹识别的相关技术，算法，历史，发展现状

运用mfcc特征参数的vq说话人识别系统。