LPC for Speech Recognition

1. 
By – Uday Saikia

2. 
Speech compression/Speech coding is a method for reducing the amount
of information needed to represent a speech signal.

LPC(Linear predictive coding)
 One
of
the
methods
of
compression
that
models
the
process of speech production.
 A digital method for encoding an analog signal in which a particular
value is predicted by a linear function of the past values of the
signal.
 At a particular time, t, the speech sample s(t) is represented as a
linear sum of the p previous samples.
 The general algorithm for linear predictive coding involves an
analysis or encoding part and a synthesis or decoding part.

3. 
In the encoding, LPC takes the speech signal in blocks or frames of
speech and determines the input signal and the coefficients of the filter
that will be capable of reproducing the current block of speech.

In the decoding, LPC rebuilds the filter based on the coefficients
received.

4. X=wavread(‘Input Signal’);
Frame=1:32;
StartSample=(Frame-1)*250+1;
EndSample=StartSample+249;
Current=x(StartSample:EndSample);
A=lpc(Current,10);
C(Frame,1)=real(a(2));
For n=2:10
S=0;
For m=1:n-1
S=s+ m*a(m+1)*c(Frame, n-m);
End;
C(Frame, n)=real(a(n+1)+s/n);
End;
For n=11:20
S=0;
For m=1:10
S=s+(n-m)*a(m+1)*c(Frame, n-m);
End;
C(Frame, n)=real(s/n);
End;
X=real(c(20,1:20));
Plot(x, ‘b*-’);

5. VOWELS
CEPSTRAL COEFFICIENT OF
MALE
CEPSTRAL COEFFICIENT
OF
FEMALE
MAXIMUM
MINIMUM
RANGE OF
VARIATION
MAXIMUM
MINIMUM
/a/
2.70
-1.42
4.12
12.29
-2.18
/e/
1.70
-0.70
2.40
1.84
-1.88
/i/
1.27
-0.74
2.01
1.84
-1.88
/o/
1.98
-0.99
2.97
1.75
-1.33
/u/
1.39
0.56
0.83
1.19
-1.19

6. Frame no.:11
10
Male
Female
8
LogMagnitude(dB)
6
4
2
0
-2
-4
0
2
4
6
8
10
12
Cepstral Coefficients
14
16
18
20
Fig i(a): Cepstral characteristics of male and female for frame no.11

7. Frame no.:12
1.5
Male
Female
LogMagnitude(dB)
1
0.5
0
-0.5
-1
0
2
4
6
8
10
12
Cepstral Coefficients
14
16
18
Fig i(b): Cepstral characteristics of male and female for frame
no.12
20

8. Frame no.:15
6
Male
Female
5
LogMagnitude(dB)
4
3
2
1
0
-1
-2
-3
0
2
4
6
8
10
12
Cepstral Coefficients
14
16
18
20
Fig i( c): Cepstral characteristics of male and female for frame no.15

9. RESULTS AND DISCUSSION
In the present study, it has been observed that by careful selection of
the feature set (Frame no 11, Frame no 12, Frame no 15) could
increase the efficiency of the recognizer. It provides a basis for Bodo
vowel recognizer. Fig i(a), Fig i(b) , Fig i(c ) have shown the prominent
distinction between male and female utterances for the Bodo vowel
/a/.From our analysis for other vowels also, distinguishable feature
vectors are found for male and female utterances for the same set of
frames.
It is thus concluded through the present study that LPCC
measure may be a better technique for speaker identification with
reference to sex of the speaker.