python实现说话人识别实验与开发

# 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](https://github.com/zhangxulong/python_tutorial_ASR/releases)