% 2009-04-07-ftrclass.diary
% Actually try classifying on some real speech data
% dpwe@ee.columbia.edu  http://www.ee.columbia.edu/~dpwe/e6820/

% Read in the phoneset definition
K = labreadkey('timit61.phset');
% Define 6 broad classes covering the 61 phones in the set
brdclass=[ones(1,13)*1,ones(1,10)*2,ones(1,7)*3,ones(1,5)*4,[2 2],ones(1,20)*5,[6 6 6 6]];
% 1=stops 2=fricatives 3=nasals 4=liquids 5=vowels 6=silence/gaps

% Look at the labeling for one TIMIT file:
% load waveform
[d,sr] = wavread('mdpk0/sa1.wav');
% load hand-label definitions (t defines times and l defines labels, 
% indexed into K)
[t,l] = labreadlab('mdpk0/sa1.phn',K);
subplot(211)
specgram(d,128,sr);
% Replace the time axis with the phone label boundaries
labplotlabs(t,l,K)

% Calculate cepstral coefficients for this example using Malcolm Slaney's 
% routine
cp = mfcc(d,16000,100);

% Set up a vector of the times corresponding to each column of cp
% (100 Hz frame rate, but 256 pt window, so first window is at 0.008 s)
tt=0.008:.01:(length(d)/sr-0.018);
% Now figure out the labels that go with each feature vector
% by 'sampling' the label ranges read from file
ll=labsamplabs(tt,t,l);

% Using the label values in ll to index into brdclass gives us the 
% broad class indices (1-6) above
subplot(212)
plot(brdclass(ll))
axis([0 length(ll) 0 7])
% Lines up with segments in spectrogram.

% Use utility functions to build up training and test sets of 
% features and labels, as above
% Define a train data set 
tdat=[];
tlab=[];
[tdat,tlab]=appenddata('mdpk0/sx153',tdat,tlab,K);
[tdat,tlab]=appenddata('mdpk0/sx243',tdat,tlab,K);
[tdat,tlab]=appenddata('mdpk0/sx333',tdat,tlab,K);
[tdat,tlab]=appenddata('mdpk0/sx423',tdat,tlab,K);
[tdat,tlab]=appenddata('mdpk0/sx63',tdat,tlab,K);
% And a test set
edat=[];
elab=[];
[edat,elab]=appenddata('mdpk0/sa1',edat,elab,K);
[edat,elab]=appenddata('mdpk0/sa2',edat,elab,K);
[edat,elab]=appenddata('mdpk0/si1053',edat,elab,K);
[edat,elab]=appenddata('mdpk0/si1683',edat,elab,K);
size(edat)
ans =
        1131          13
size(tdat)
ans =
        1453          13

% Try a classification based on the first 8 cepstra
[H,O,R]=doclassif(tdat(:,1:8),brdclass(tlab),edat(:,1:8),brdclass(elab));
Ep 1 lr=0.4 frame accuracy on trn = 78.6648% test = 72.5022%
Ep 2 lr=0.2 frame accuracy on trn = 77.7013% test = 66.7551%
Ep 3 lr=0.1 frame accuracy on trn = 79.3531% test = 71.176%
% Do it again
[H,O,R]=doclassif(tdat(:,1:8),brdclass(tlab),edat(:,1:8),brdclass(elab));
Ep 1 lr=0.4 frame accuracy on trn = 80.3166% test = 71.9717%
Ep 2 lr=0.2 frame accuracy on trn = 80.4542% test = 72.1485%
Ep 3 lr=0.1 frame accuracy on trn = 81.1425% test = 70.4686%
% Several percent variation in successive runs is not unusual

% Check confusion matrix
confus(brdclass(elab),R)
ans =
    58     5     7     0    13    31
    17   112     1     1    24     2
     5     1    46     0     8     0
    15     1     9    17    49     0
     8     3     6    21   367     2
    52    43     0     1     9   197
% Vowels and fricatives are distinct; silence and stops confused