Es25893896

Prof. Vijaya sughandhi, Prof. D.K.Rai / International Journal of Engineering Research and
Applications (IJERA) ISSN: 2248-9622 www.ijera.com
Vol. 2, Issue 5, September- October 2012, pp.893-896
Statistical Evaluation In Speech Processing Techniques

Prof. Vijaya sughandhi1 Prof. D.K.Rai2
Dept. of Electrical & Elex. Engg. Dept. of Electrical & Elex. Engg.
Shri VaishnavSM Institute of Tech. Shri VaishnavSM Institute of Tech.
& science, Indore (M.P.)-India & science, Indore (M.P.)-India

ABSTRACT
The objective of this paper is to generate II. BACKGROUND
a reconstructed output speech signal from the The term speech processing basically refers
input signal involving the application of a filter to the scientific discipline concerning the analysis
estimation technique. In this paper, filter is used and processing of speech signals in order to achieve
to estimate the parameters represented in the the best benefit in various practical scenarios [2]. The
state-space domain, in which the speech signal is field of speech processing is, at present, undergoing a
modeled as such. rapid growth in terms of both performance and
The results of this speech coding applications. This is stimulated by the advances being
technique are demonstrated and obtained with the made in the field of microelectronics, computation
help of MATLAB. Accurate estimations by the and algorithm design [3].
filter on speech is simulated and presented.
Comparison between the assigned values and the Sampling
estimated values of the parameters is described. The purpose of sampling is to transform an
The reconstruction of speech and the input speech analog signal that is continuous in time to a sequence
is also compared for error estimations. of samples discrete in time. The signals we use in the
Key words: filter, speech coding technique. real world, such as our voices, are called "analog"
signals. In order to process these signals in
1. INTRODUCTION computers, most importantly it must be converted to
Speech processing has been a growing and "digital" form. While an analog signal is continuous
dynamic field for more than two decades and there is in both time and amplitude, a digital signal is discrete
every indication that this growth will continue and in both time and amplitude. Since in this thesis,
even accelerate. During this growth there has been a speech will be processed through a discrete Kalman
close relationship between the development of new filter, it is necessary for converting the speech signal
algorithms and theoretical results, new filtering from continuous time to discrete time, hence this
techniques are also of consideration to the success of process is described as sampling.
speech processing. One of the common adaptive
filtering techniques that are applied to speech is the The value of the signal is measured at
Wiener filter. This filter is capable of estimating certain intervals in time. Each measurement is
errors however at only very slow computations. On referred to as a sample. Once the continuous analog
the other hand, the Kalman filter suppresses this speech signal is sampled at a frequency f, the
disadvantage. resulting discrete signal will have more frequency
According to [1], Kalman filter is so popular components than the analog signal. To be precise, the
in the field of radar tracking and navigating system is frequency components of the analog signal are
that it is an optimal estimator, which provides very repeated at the sample rate. Explicitly, in the discrete
accurate estimation of the position of either airborne frequency response they are seen at their original
objects or shipping vessels. Due to its accurate position, and also centered around +/- f, and +/- 2 f,
estimation characteristic, electrical engineers are etc.
picturing the Kalman filter as a design tool for The signal still preserve the information, it is
speech, whereby it can estimate and resolve errors necessary to sample at a higher rate greater than
that are contained in speech after passing through a twice the maximum frequency of the signal. This is
distorted channel. Due to this motivating fact, there known as the Nyquist rate. The Sampling Theorem
are many ways a Kalman filter can be tuned to suit states that a signal can be exactly reconstructed if it is
engineering sampled at a frequency f, where f > 2fm where fm is
maximum frequency in the signal.
Applications such as network telephony and even
satellite phone conferencing.
III. PROPOSED METHODOLOGY
From a statistical point of view, many
signals such as speech exhibit large amounts of

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correlation. From the perspective of coding or signal Yk. That is in order to construct Yk, we will
filtering, this correlation can be put to good use [6]. need matrix X that contains the Kalman coefficients
The all pole, or autoregressive (AR), signal model is and the white noise, wk which both are obtained from
often used for speech. From [4], the AR signal model the estimation of the input signal. This information is
is introduced as: enough to determineHHk-1.
yk = a1yk-1 + a2yk-2 + ……. + aNyk-N + wk (3.1)
where, Where
k = Number of iterations;
yk= current input speech signal sample; HHk-1= 3.10
yk=(N-1)th sample of speech signal;
aN =Nth Kalman filter coefficient;
and wk = excitation sequence (white noise). Thus, forming the equation (3.10) mentioned above.
In order to apply Kalman filtering to the speech
expression shown above, it mustbe expressed in state IV. RESULTS
space form as This approach is to prove that Kalman filter
Hk = X Hk-1 + Wk (3.2) functions properly in MATLAB 7. Reconstructed
yk = g Hk (3.3) accurate sound output are shown in fig.4.1.The
results shown below from Fig 4.2 to Fig 4.6, the
X is the system matrix, Hk consists of the Kalman filter generates a very close set of
series of speech samples; Wk is the excitation vector coefficients, which are similar to those, selected
and g, the output vector. The reason of (k-N+1)th above in (4.2). From the following results, accurate
iteration is due to the state vector, Hk, consists of N estimation of the coefficients took several hundreds
samples, from the kth iteration back to the(k-N+1)th of iterations in order to stabilize to the required value.
iteration. The reason for this is that Kalman filter works in a
loop fashion. In order to obtain a more accurate
The above formulations are suitable for the estimate, it will need to go through more “predict”
Kalman filter. As mentioned in the previous chapter, and “correct” procedures
the Kalman filter functions in a looping method. 0.4
reconstruct output of sound1

Referring to [5] as a guide in implementing Kalman
0.3
filter to speech, we denote the following steps within
the loop of the filter. Define matrix HT k-1 as the row 0.2

vector: 0.1

HTk-1 = -[yk-1 yk-2 … yk-N] (3.4) 0
v(k)

and zk = yk. Then (3.1) and (3.4) yield -0.1
zk = HTk-1 Xk + wk ( 3.5)
-0.2
where Xk will always be updated according
to the number of iterations, k. when the k = 0, the -0.3

matrix Hk-1 is unable to be determined. However, -0.4

when the time zk is detected, the value in matrix Hk-1 -0.5
0 500 1000 1500 2000 2500 3000 3500 4000 4500 5000
is known. The above purpose is thus sufficient Number of Iterations

enough for defining the Kalman filter, which consists Fig. 4.1 Output of reconstructed signal
of: Estimated Filter
Xk = [ I – KkHTk-1] Xk-1 + Kk zk (3.6) 0
Where I= Identity matrixwith
-0.1
Kk = Pk-1Hk-1[ HTk-1Pk-1 Hk-1 + R ]-1 (3.7)
where Kk is the Kalman gain matrix, Pk-1 is the a -0.2
priori error covariance matrix,R is measurement
-0.3
noise covariance.and
Pk = Pk-1 - Pk-1 Hk-1 [HTk-1 Pk-1 Hk-1 + R]-1.HTk-1Pk-1 + Q -0.4
XX(1,k)

where Pk is the a posteriori error covariance matrix,
-0.5
and Q is identity matrix.
Thereafter the reconstructed speech signal, Yk after -0.6
Kalman filtering will be formed in a manner similar
to (3.1): -0.7

Yk = a1Yk-1 + a2Yk-2 + ……. + aNYk-N + wk (3.9) -0.8
Since the value of Yk is the input at the beginning of
the process, there will be no problem forming HTk-1. -0.9
0 500 1000 1500 2000 2500 3000 3500 4000 4500 5000
In that case a question rises, how is Yk formed? The Number of Iterations
parameters wk and {ai}i=1 Nare determined from Fig. 4.2 First coefficient of filter
application of the Kalman filter to the input speech

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Estimated Filter
0.25
Estimated Filter
0.05
0.2
0
0.15
-0.05
0.1
-0.1

0.05 -0.15
XX(2,k)

XX(5,k)
0 -0.2

-0.25
-0.05
-0.3
-0.1
-0.35
-0.15
-0.4
-0.2
-0.45
0 500 1000 1500 2000 2500 3000 3500 4000 4500 5000
-0.25 Number of Iterations
0 500 1000 1500 2000 2500 3000 3500 4000 4500 5000
Number of Iterations Fig. 4.6 Fifth coefficient of filter
Fig. 4.3 Second coefficient of filter

V. CONCLUSION
Estimated Filter
In this paper, an implementation of
0.1 employing Kalman filtering to speech processing had
been developed. As has been previously mentioned,
0 the purpose of this approach is to reconstruct an
output speech signal by making use of the accurate
-0.1 estimating ability of the Kalman filter. Simulated
results from the previous section had proven that the
-0.2
Kalman filter has the ability to estimate accurately.
XX(3,k)

-0.3
Acknowledgment
This author thanks to their family and
-0.4
friends for their motivation support and
encouragement.
-0.5
Reference:
[1] M.S. Grewal and A.P. Andrews, Kalman
-0.6
Filtering Theory and Practice Using
MATLAB 2nd eition, John Wiley & Sons,
-0.7
0 500 1000 1500 2000 2500 3000 3500 4000 4500 5000 Canada, 2001, pp 15-17
Number of Iterations [2] R. P. Ramachandran and R. Mammone,
Fig. 4.4 Third coefficient of filter Modern Methods of Speech Processing,
Kluwer Academic Publishers,
Massachusetts, USA, 1994.
Estimated Filter
[3] A.N. Ince, Digital Speech Coding, Kluwer
0.8 Academic Publishers, Massachusetts, USA,
0.7 1992.
0.6
[4] S. Crisafulli, J.D. Mills, and R.R Bitmead,
“Kalman Filtering Techniques in Speech
0.5
Coding”. In Proc. IEEE International
0.4
Conference on Acoustics, Speech, and
XX(4,k)

0.3 Signal Processing, San Francisco, March
0.2 1992.
[5] B.D.O. Anderson and J.B. Moore, “The
0.1
Kalman Filter,” Optimal Filtering, Prentice
0
Hall Inc., Englewood Cliffs, N.J., 1979, pp.
-0.1 50-52.
-0.2 [6] C. R. Watkins, “Practical Kalman Filtering
0 500 1000 1500 2000 2500 3000
Number of Iterations
3500 4000 4500 5000
in Signal Coding”, New Techniques in
Signal Coding, ANU, Dec 1994.
Fig. 4.5 Fourth coefficient of filter

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Ms.Vijaya Sugandhi received the B.E.
.degree and M.E.(digital techniques
&instrumentation) Degree with in Electrical
engineering in 2006 and 2011 both from Rajiv
Gandhi Technical University, Bhopal,
Madhypradesh, India

Mr. Dev Kumar Rai received the B.E. .degree
(with Hon’s) and M.E.(Power Electronics)
Degree with in Electrical engineering in 2004
and 2010 both from Rajiv Gandhi Technical
University, bhopal, Madhyapradesh, India

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