Speech Recognition.rar_in_recognition Word_speech recognition

% % Word recognition in MATLAB % % Author : Dr. Selvaraaju Murugesan, LaTrobe University, Ausralia % % Date : 09-07-2014 % % traning phase clc; clear;close all w = warning ('off','all'); Fs=8000; % % For the word "ONE" filepart1='myrecordone'; filepart2='.wav'; % % check for signal length % % we will append zeros after VAD to make sure all the signals are of % % equal length % % We need same number of MFCC of each signal length_all_sig=[]; for i=1:10 filename=strcat(filepart1,num2str(i),filepart2); y1=wavread(filename); results = VAD(y1,0.1,0.025,0.0125,20,1); ind_st=(0:size(results)-1)*200+1; ind_en=(1:size(results))*200; ind1=ind_st(logical(results)); ind2=ind_en(logical(results)); all_ind=cell2mat(arrayfun(@colon,ind1,ind2,'uni',0)); filt_signal=y1(all_ind); length_all_sig=[length_all_sig length(filt_signal)]; end max_length=max(length_all_sig); all_data_one=[]; for i = 1:10 filename=strcat(filepart1,num2str(i),filepart2); y1=wavread(filename); results = VAD(y1,0.1,0.025,0.0125,20,1); ind_st=(0:size(results)-1)*200+1; ind_en=(1:size(results))*200; ind1=ind_st(logical(results)); ind2=ind_en(logical(results)); all_ind=cell2mat(arrayfun(@colon,ind1,ind2,'uni',0)); filt_signal=y1(all_ind); if length(filt_signal)==max_length [cepstra1,aspectrum,pspectrum] = melfcc(y1,Fs,'wintime',0.025,'hoptime',0.010); else filt_signal=[filt_signal' zeros(1,max_length-length(filt_signal))]; [cepstra1,aspectrum,pspectrum] = melfcc(filt_signal,Fs,'wintime',0.025,'hoptime',0.010); end all_data_one=[all_data_one cepstra1]; end % % For the word "TWO" all_data_two=[]; filepart1='myrecordtwo'; length_all_sig=[]; for i=1:10 filename=strcat(filepart1,num2str(i),filepart2); y1=wavread(filename); results = VAD(y1,0.1,0.025,0.0125,20,1); ind_st=(0:size(results)-1)*200+1; ind_en=(1:size(results))*200; ind1=ind_st(logical(results)); ind2=ind_en(logical(results)); all_ind=cell2mat(arrayfun(@colon,ind1,ind2,'uni',0)); filt_signal=y1(all_ind); length_all_sig=[length_all_sig length(filt_signal)]; end max_length=max(length_all_sig); for i = 1:10 filename=strcat(filepart1,num2str(i),filepart2); y1=wavread(filename); results = VAD(y1,0.1,0.025,0.0125,20,1); ind_st=(0:size(results)-1)*200+1; ind_en=(1:size(results))*200; ind1=ind_st(logical(results)); ind2=ind_en(logical(results)); all_ind=cell2mat(arrayfun(@colon,ind1,ind2,'uni',0)); filt_signal=y1(all_ind); if length(filt_signal)==max_length [cepstra2,aspectrum,pspectrum] = melfcc(y1,Fs,'wintime',0.025,'hoptime',0.010); else filt_signal=[filt_signal' zeros(1,max_length-length(filt_signal))]; [cepstra2,aspectrum,pspectrum] = melfcc(filt_signal,Fs,'wintime',0.025,'hoptime',0.010); end all_data_two=[all_data_two cepstra2]; end % % For the word "THREE" all_data_three=[]; filepart1='myrecordthree'; length_all_sig=[]; for i=1:10 filename=strcat(filepart1,num2str(i),filepart2); y1=wavread(filename); results = VAD(y1,0.1,0.025,0.0125,20,1); ind_st=(0:size(results)-1)*200+1; ind_en=(1:size(results))*200; ind1=ind_st(logical(results)); ind2=ind_en(logical(results)); all_ind=cell2mat(arrayfun(@colon,ind1,ind2,'uni',0)); filt_signal=y1(all_ind); length_all_sig=[length_all_sig length(filt_signal)]; end max_length=max(length_all_sig); for i = 1:10 filename=strcat(filepart1,num2str(i),filepart2); y1=wavread(filename); results = VAD(y1,0.1,0.025,0.0125,20,1); ind_st=(0:size(results)-1)*200+1; ind_en=(1:size(results))*200; ind1=ind_st(logical(results)); ind2=ind_en(logical(results)); all_ind=cell2mat(arrayfun(@colon,ind1,ind2,'uni',0)); filt_signal=y1(all_ind); if length(filt_signal)==max_length [cepstra3,aspectrum,pspectrum] = melfcc(y1,Fs,'wintime',0.025,'hoptime',0.010); else filt_signal=[filt_signal' zeros(1,max_length-length(filt_signal))]; [cepstra3,aspectrum,pspectrum] = melfcc(filt_signal,Fs,'wintime',0.025,'hoptime',0.010); end all_data_three=[all_data_three cepstra3]; end % % Building model X=[all_data_one']; options = statset('MaxIter',500,'Display','final'); obj1 = gmdistribution.fit(X,8,'CovType',... 'diagonal','Options',options); X=[all_data_two']; obj2 = gmdistribution.fit(X,8,'CovType',... 'diagonal','Options',options); X=[all_data_three']; obj3 = gmdistribution.fit(X,8,'CovType',... 'diagonal','Options',options); % % Test data test_data=cepstra1'; % test_data=cepstra1'; % test_data=cepstra2'; % % Word recognition [~,nlogl1] = posterior(obj1,test_data); [~,nlogl2] = posterior(obj2,test_data); [~,nlogl3] = posterior(obj3,test_data); log_like=[nlogl1 nlogl2 nlogl3]; [~,Spoken_word]=min(log_like)