50320130403005 2

International Journal of Information Technology & Management Information System (IJITMIS), ISSN
INTERNATIONAL JOURNAL OF INFORMATION TECHNOLOGY &
0976 – 6405(Print), ISSN 0976 – 6413(Online) Volume 4, Issue 3, September - December (2013), © IAEME

MANAGEMENT INFORMATION SYSTEM (IJITMIS)

ISSN 0976 – 6405(Print)
ISSN 0976 – 6413(Online)
Volume 4, Issue 3, September - December (2013), pp. 68-84
© IAEME: http://www.iaeme.com/IJITMIS.asp
Journal Impact Factor (2013): 5.2372 (Calculated by GISI)
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IJITMIS
©IAEME

DESIGN A TEXT-PROMPT SPEAKER RECOGNITION SYSTEM
USING LPC-DERIVED FEATURES
Dr. Mustafa Dhiaa Al-Hassani1, Dr. Abdulkareem A. Kadhim2
1

2

Computer Science/Mustansiriyah University, Baghdad, Iraq
College of Information Technology/Al-Nahrain University, Baghdad, Iraq

ABSTRACT
Humans are integrated closer to computers every day, and computers are taking over
many services that used to be based on face-to-face contact between humans. This has
prompted an active development in the field of biometric systems. The use of biometric
information has been known widely for both person identification and security applications.
The paper is concerned with the use of speaker features for protection against unauthorized
access. A speaker recognition system for 6304 speech samples is presented that relies on LPCderived features. A vocabulary of 46 speech samples is built for 10 speakers, where each
authorized person is asked to utter every sample 10 times. Two different modes are considered
in identifying individuals according to their speech samples. In the closed-set speaker
identification, it is found that all tested LPC-derived features outperform the raw LPC
coefficients and 84% to 97% identification rates are achieved. Applying the preprocessing steps
to the speech signals (preemphasis, remove DC offset, frame blocking, overlapping,
normalization and windowing) improve the representation of speech features, and up to 100%
identification rate was obtained using weighted Linear Predictive Cepstral Coefficients
(LPCC). In the open-set speaker verification mode of our proposed system model, the system
selects randomly a pass phrase of 8-samples length from its database for each trial a speaker is
presented to the system. Up to 213 text-prompt trials from 23-different speakers (authorized
and unauthorized) are recorded (i.e., 1704 samples) in order to study the system behavior and
to generate the optimal threshold in which the speakers are verified or not when compared to
those training references of authorized speakers constructed in the first mode, where the best
obtained speaker verification rate is greater than 99%.
Keywords: Biometric, LPC-derived features,
Identification, Speaker Verification, Text-prompt.
68

LSF,

Speaker

Recognition,

Speaker


I. INTRODUCTION
As everyday life is getting more and more computerized, automated security systems
are getting more and more important. Today most personal banking tasks can be performed
over the Internet and soon they can also be performed on mobile devices such as cell phones
and PDAs. The key task of an automated security system is to verify that the users are in fact
those who claim to be [1].
Since the level of security breaches and transaction fraud increases, the need for highly
secure identification and personal verification technologies is becoming apparent. Biometricbased solutions are able to provide confidential financial transactions and personal data privacy
[2]. The need for biometrics can be found in federal, state and local governments, in the
military, and in commercial applications [1, 3]. A biometric system is essentially a pattern
recognition system that establishes the authenticity of a specific physiological or behavioral
characteristic possessed by a user. They are typically based on some single biometric feature of
humans, but several hybrid systems also exist [2, 4, 5, 1, 6].
Human voice can serve as a key for any security objects, and it is not easy to lose or
forget it. This technique can be used to verify the identity claimed by people accessing systems;
that is, it enables control of access to various services by voice [3, 7]. Speaker recognition has
received for many years the attention of researchers working in the field of signal processing.
This technology has been developed in such a way that it can be used in a number of
applications, such as: voice dialing, banking over a telephone network, person authentication,
remote access to computers, command and control systems, network security and protection,
entry and access control systems, data access/information retrieval, Monitoring, … etc [8, 5, 9,
10, 11].
II. AIM OF THE WORK
This work aims to build a speaker recognition (identification/verification) system that
automatically authenticate a speaker's identity by his/her voice, according to a random textprompt generated by the system, and then gives only the authorized persons a privilege or an
access right to the facility that need to be protected from the intrusion of unauthorized persons.
III. THE PROPOSED SPEAKER RECOGNITION SYSTEM MODEL
In this section, several linear prediction based methods (LPC, PARCOR, LAR, ASRC,
LPCC, and LSF) are tested for text-dependent speaker recognition system in a closed-set mode.
The open-set speaker verification mode is also investigated, which involves speaker’s
verification according to a randomly text-prompt sentence generated by the system. The block
diagram for the proposed speaker recognition system model, shown in Fig. (1), illustrates that
the input speech is passed through six preprocessing operations (preemphasis, remove DC
offset, frame blocking, overlapping, normalization and windowing) prior to feature extraction
phase. If the match is lower than certain threshold, then the identity claims is verified
"Accepted", otherwise, the speaker is "Rejected" [1].

69


Figure (1): Block-Diagram of the proposed Speaker Recognition System Model
3.1. Speech Recording
Any speaker recognition system depends on speech recording samples as input data.
The speech signals used for training and testing are recorded in a quiet (but not a soundproof)
rooms via high quality built-in microphone and digitized by a sound card of type
Crystal − Intel (r) integrated audio using DELL Latitude C400 Notebook and having the
following recording features: .wav file format, 11 kHz sampling rate, 2-bytes/sample and single
channel [1].
3.2. Database Construction
In this work, database samples were recorded in two modes of operation:
Closed-set speaker identification mode
Open-set speaker verification mode
In order to evaluate the identification/verification performance of the proposed system model,
each speaker is asked to utter the vocabulary data sets, shown in Table-1, for a maximum of 10
utterances/sample.
The number of repetition R ( 1≤ R < 10 ) can be considered as training set during an
enrollment phase to train the speaker’s model of authorized persons, and the other ( 10 – R )
repetitions are considered for testing during a matching phase to classify them with those
training references in the database. As a result, the total database size of speaker’s samples for
this mode is [1]:
Total DB S ize = 10 × No. of Samples × No. of Spea ker s

(1)

No. of Training Re ferences = R × No.of Samples × No. of Speaker s

(2)

No. of Test Samples = (10 − R) × No. of Samples × No. of Spea ker s

(3)

70


Table-1: The recorded speech samples
Data Sets

Speech Samples

1) Digits

0 ... 9

2) Characters
3) Words

‘A’ ... ‘Z’
Accept, Reject, Open, Close, Help,
Computer, Yes, No, Copy, Paste

For practical purposes, these data sets are very interesting because the similarities
between several samples (especially letters) lead to the realization of important problems in
speech recognition.
In the closed-set speaker identification mode, up to 4600 samples were collected from
different persons, whereas 1704 samples were recorded in the open-set speaker verification
mode.
In the open-set speaker verification mode of our proposed system model, the system
selects randomly a pass phrase of 8-samples length from its database for each trial a speaker is
presented to the system. Up to 213 text-prompt trials from different speakers (i.e., authorized
and unauthorized) are recorded (i.e., 1704 samples) in order to study the system behavior. In
fact, the generated text-prompt sentence, shown in Table-2, is a random number between 1 and
46 which corresponds to the samples in the vocabulary shown in Table-1. This is performed in
order to study the system behavior and to generate the optimal threshold in which the speakers
are verified to be accepted or not when compared to those training references of authorized
speakers constructed in the first mode [1].
Table-2: Examples of Randomly Text-Prompt Sentences generated by the System

Table-2 illustrates five examples of text-prompt sentences generated by the system
where column Si ( i=1,2,...,8 ) stands for sample number i , which compose a sentence in each
row [1].

71


3.3. Preprocessing
The basic idea behind speech preprocessing is to generate a signal with a fine structure
as close as possible to that of the original speech signal. This produces a data reduction facility
with easier task analysis [11]. A number of processing techniques adopted in this system model
are applied in the following sequence:

• Preemphasis
Usually the digital speech signal, s[n], is preemphasized first. This is achieved by
passing the signal through a high-pass filter. This process emphasis the high frequencies
relative to low frequencies, hence, compensating the effect of band limiting the input signal
with a low-pass filter in the recording process. The most commonly used preemphasis filter is
given by the following transfer function [12, 13, 10, 14]:
(4)
where α typically lies in the range of 0.9 ≤ α < 1.0 , which controls the slope of the filter
that is simply implemented as a first order differentiator:
(5)
For the proposed system model α is set to 0.95 [1].

• The Removal of DC offset
DC offset occurs when hardware, such as a sound card, adds DC current to a recorded
audio signal. This current produces a recorded waveform that is not centered on the baseline.
Therefore, removing this DC offset is the process of forcing the input signal mean to the
baseline by adding a constant value to the samples in the sound file. An illustrative example
of removing DC offset from a waveform file is shown in Fig. (2) [1].

Figure (2): Removal of DC offset from a Waveform file (a) Exhibits DC offset,
(b) After the removal of DC offset

• Frame-Blocking
It is the process of blocking or splitting the input speech samples into equal durations of
N samples length to carryout frame-wise analysis. The selection of the frame length is a crucial
parameter for successful spectral analysis, due to the trade-off between the time and frequency
resolutions. The window should be long enough for adequate frequency resolution, but on the
other hand, it should be short enough so that it would capture the local spectral properties.
Typically a frame length of 10 − 30 milliseconds is used. The signal for the i-th frame is given
by [15, 14, 10, 12]:
72


(6)
In this work, a frame length N = 256 samples with a duration of 23.2 milliseconds is used [1].

• Overlapping
Usually adjacent frames are overlapped. The frame is shifted forward by a fixed
amount, typically 30 – 50 % of the frame length along the signal. The purpose of the
overlapping is to avoid losing of information since that each speech sound of the input
sequence would be approximately centered at some frame [1, 15, 13, 16].
• Normalization
The frames of speech are normalized to make their power equal to unity. This step is
very important since the extracted frames have different intensities due to the speaker loudness,
speaker distance from the microphone and recording level. The normalization is done by
dividing each sample by the square root of the sum of squares of all the samples in the segment
as stated below:
(7)
where S[n] is the speech sample, N is the number of samples in the segment which is
256, and the subscript norm refers to normalization [1].

• Windowing
The purpose of windowing is to reduce the effect of spectral-leakage (type of distortion
in spectral analysis) that results from the framing process. Windowing involves multiplying a
speech signal x(n) by a finite-duration window w(n), which yields a set of speech samples
weighted by the shape of the window, as stated by the following equation [1, 15, 13, 17, 12]:
(8)
where N is the size of the window or frame.
There exist many different windowing functions; Table-3 lists the window functions
that are used in our experiments and their shapes illustrated in Fig. (3) [1].
Table-3: Rectangular, Hamming and Kaiser Window-Function

73


Figure (3): Rectangular, Hamming and Kaiser Window-Function of 256 Samples Length
[1]
3.4. Feature Extraction
Having acquired the testing or training utterances, it is now the role of the feature
extractor to extract the features from the speech samples. Feature extraction refers to the
process of reducing dimensionality by forming a new “smaller” set of features from the original
feature set of the patterns. This can be done by extracting some numerical measurements from
raw input patterns [8, 1, 15]. Several linear prediction based features are tested, which include
LPC, PARCOR, LAR, ASRC, LPCC, and LSF.
• Linear Predictive Coding (LPC)
Linear prediction (LP) forms an integral part of almost all modern day speech coding
algorithms. The fundamental idea is that a speech sample can be approximated as a linear
combination of past samples. Within a signal frame, the weights used to compute the linear
combination are found by minimizing the mean-squared prediction error; the resultant weights,
or linear prediction coefficients (LPCs), are used to represent the particular frame [18]. The
importance of this method lies in its ability to provide extremely accurate estimates of the
speech parameters, and in its relative speed of computation [20, 19].
The LPC model, assumes that each sample s(n) at time n, can be approximated by a
linear sum of the p previous samples
p

s[ n ] ≈

∑

a [ k ] s[ n − k ]

k =1

(9)

where s[n] is an approximation of the present output, s[n−k] are past outputs, p is the prediction
order; and {a[k]}, k = 1...p are the model parameters called the predictor coefficients that need
to be determined so that the average prediction error (or residual) is as small as possible [10,
19].
The prediction error for nth sample is given by the difference between the actual sample
and its predicted value [1, 13, 20, 10]:
p

e[ n ] = s[ n] − ∑ a[ k ] s[ n − k ]
k =1

74

(10)


Equivalently,

p

s[ n ] = ∑ a[ k ] s[ n − k ] + e[ n ]

(11)

k =1

When the prediction residual e[n] is small, predictor Eq. (9) approximates s[n] well.
The total squared prediction error is given by
E =

∑ e[ n ]

2

n
p

=

∑ ( s[ n ] − ∑ a [ k ] s [ n − k ])

2

(12)

k =1

n

Minimization of error is achieved by setting the partial derivatives of E with respect to
the model parameters {a[k]} to zero:
∂E
= 0, k = 1,..., p
(13)
∂ a[ k ]
By writing out Eq. (13) for k = 1 ... p, the problem of finding the optimal predictor
coefficients reduced to solve of so-called (Yule- Walker) AR equations. Depending on the
choice of the error minimization interval in Eq. (12), there are two methods for solving the AR
equations: covariance method and autocorrelation method [13, 10, 19]. The two methods do not
have large difference, but the autocorrelation method is the preferred since it is computationally
more efficient and it always guarantees a stable filter.
In matrix form, the set of linear equations is represented by Ra = v which can be
rewritten as [13, 1, 21]:

R

a

v

 R(0)
R(1) K R(p−1)   a1   R(1) 

  

R(0) K R(p− 2)  a2   R(2) 
 R(1)
=
 M
M
O
M  M   M 

  

R(p−1) R(p− 2) K R(0)   ap   R(p) 

  


(14)

where R is a special type of matrix called Toeplitz matrix (symmetric with all diagonal
elements equal, this facilitates the solution of the Yule-Walker equations for the LP coefficients
{ak} through computationally fast algorithms such as the Levinson – Durbin algorithm), a is
the vector of the LPC coefficients and v is the autocorrelation. Both the matrix R and vector v
are completely defined by p autocorrelation samples. The autocorrelation sequence of s[n] is
defined as [1, 21, 10, 19, 13]:

R[ k ] =

1
N

N −1− k

∑ s[ n ] s [ n + k ]
n=0

(15)

where N is the number of data points in the segment.
Due to the redundancy in the Yule-Walker (AR) equations, there exists an efficient
algorithm for finding the solution, known as Levinson-Durbin recursion [1, 10, 19, 20, 13].
75


E ( 0 ) = R( 0 )
ki
ai( i )

(16)



=  R( i ) −


= ki

i −1

∑a

( i −1 )
j

j =1



R( i − j ) E( i − 1 ) , 1 ≤ i ≤ p



a(j i ) = a(j i −1 ) − ki ai(−i −1 ) ,
j

1 ≤ j ≤ i −1

E ( i ) = ( 1 − ki2 ) E ( i −1 )

(17)

(18)
(19)

where
k i : Partial Correlation Coefficients (PARCOR).
a j ( i ) : is the jth predictor (LPC) coefficient after i iterations.
E ( i ) : is the prediction error after i iterations.
The Levinson-Durbin procedure takes the autocorrelation sequence as its input, and
produces the coefficients a[k]; k = 1… p. The time complexity of the procedure is O(p2) as
opposed to standard Gaussian elimination method whose complexity is O(p3). Equations (16 –
19) are solved recursively for i = 1, 2, …, p, where p is the order of the LPC analysis and the
final solution is given as [13, 1, 10, 20, 19]:

aj = aj( p) ,

1≤ j ≤ p

(20)

• Partial Correlation Coefficients (PARCOR)
Several alternative representations can be derived from LPC coefficients when the
autocorrelation method is used. The Levinson-Durbin algorithm produces the quantities {[k i ]};
i = 1, 2, … p (are in the range of -1 ≤ k i ≤ 1), which is known as the reflection or PARCOR
coefficients [13, 1].
• Log Area Ratio (LAR)
A new parameter set, which can be derived from the PARCOR coefficients, is obtained
by taking the logarithm of the area ratio, yielding log area ratios (LARs) {g i } defined as [19,
20, 22, 10, 1, 13].

 1 − ki 
g i = log 
(21)
1+ k , 1 ≤ i ≤ p


i 
• Arcsin Reflection Coefficients (ASRC)
An alternative for the log area ratios are arcsin reflection coefficients, simply computed
as taking the sine inverse of the reflection coefficients [10, 1, 13].

arcsin i = sin −1 ( k i ) ,

76

1≤ i ≤ p

(22)


• Linear Predictive Cepstral Coefficients (LPCC)
An important fact is that cepstrum can also be derived directly from the LPC parameter
set. The relationship between cepstrum coefficients c n and prediction coefficients a k is
represented in the following equations [1, 9, 13]:
c1 = a1
n−1

cn = ∑ (1 − k / n ) . ak . cn−k + an , 1 < n ≤ p

(23)

k =1

where p is a prediction order. It is usually said that the cepstrum, derived in such a way
represents the “smoothed” version of the spectrum. Similar to LPC analysis, increasing the
number of coefficients results in more details [10, 4].
Because of the sensitivity of the low-order cepstral coefficients to overall spectral slope
and the sensitivity of the high-order cepstral coefficients to noise (and other forms of noise-like
variability), it has become a standard technique to weight the cepstral coefficients by a tapered
window so as to minimize these sensitivities and improving the performance of these
coefficients [19, 14, 13, 1]. To achieve the robustness for large values of n, it must consider a
more general weighting of the form:
where

)
c (n) = c(n) × w(n) , 1 ≤ n ≤ p

(24)

 P
πn 
w(n) = 1 + sin(
) ,
p 
 2


(25)

1≤ n ≤ p

This weighting function truncates the computation and deemphasis c n around n = 1 and
around n = P [19].
• Line Spectral Frequencies (LSFs)
Another representation of the LP parameters of the all-pole spectrum is the set of line
spectral frequencies (LSF’s) or line spectrum pairs (LSP’s) [23, 21]. It is proposed to be
employed in speech compression and other audio signals, which is the most widely
representation of LPC parameters used for quantization and coding but they have been applied
with good results to speaker recognition [23, 24, 10, 1]. LSFs are the roots of the following
polynomials:

P(z) = B(z ) + z -(p+ 1 ) B(z -1 )

Q(z) = B(z ) - z -(p+ 1 ) B(z -1 )

(26)
(27)

where B(z) = 1/H(z) = 1 − A(z) is the inverse LPC filter. The roots of P(z) and Q(z) are
interleaved and occur in complex-conjugate pairs so that only p/2 roots are retained for each of
P(z) and Q(z) (p roots in total). Also, the root magnitudes are known to be unity and, therefore,
only their angles (frequencies) are needed.
Each root of B(z) corresponds to one root in each of P(z) and Q(z). Therefore, if the
frequencies of this pair of roots are close, then the original root in B(z) likely represents a
formant, and, otherwise, this latter root represents a wide bandwidth feature of the spectrum.
These correspondences provide us with an intuitive interpretation of the LSP coefficients [13].

77


3.5. Pattern Matching
The resulting test template, which is an N-dimensional feature vector, is compared
against the stored reference templates to find the closest match. The process is to find which
unknown class matches a predefined class or classes. For the speaker identification task, the
unknown speaker is compared to all references in the database. This comparison can be done
through Euclidean (E.D.) or city-block (C.D.) distance measures [1, 25, 26], as shown below:
N

∑1 ( a i
i=

E .D . =

C .D . =

N

∑

i=1

a

i

(28)

− bi ) 2

− bi

(29)

where A and B are two vectors, such that A = [a1 a2 … aN] and B = [b1 b2 … bN].
3.6. Decision Rule
The decision rule process is to select the pattern that best match the unknown one. The
primary methods for the discrimination process are either to measure the difference between
the two feature vectors or to measure the similarity. In our approach the minimum distance
classifier, by measuring the difference between the two patterns, is used for speaker
recognition. This classifier assigns the unknown pattern to the nearest predefined pattern. The
bigger distance between the two vectors, is the greater difference. On the other hand, the
identity of the unknown speaker was verified by considering the best matched reference in the
database where their distance is lower than a certain threshold [1, 25, 26].
IV. EXPERIMENTAL RESULTS
Many experiments and test conditions were accomplished to measure the performance
of the proposed system with different criterions concerning: preemphasis, frame overlapping,
LPC order, window type, cepstral weighting and the text-prompt speaker verification.
The identification rate is defined as the ratio of correct identified speakers to the total
number of test samples which corresponds to a nearest neighbor decision rule.

Identifica tion Rate =

No. of Correctly Identified Spea ker s
Total No. of Samples Tested

× 100 %

(30)

4.1. Identification Rate for LP based Coefficients
A more appropriate comparison can be made if the entire LP based coefficients methods
(LPC, PARCOR, LAR, ASRC, LPCC and LSF) are measured under identical conditions (the
order of LP based coefficients P = 15, remove DC offset, no overlap between successive
frames, normalization, rectangular window type). The classification results are shown in Table4 and its equivalent chart Fig. (4).

78


Table-4: Identification Rate for the LP based Coefficients
Euclidean Distance City-block Distance
(C. D.)
(E. D.)
84.173
84.217
LPC
95.173
95.260
PARCOR
94.000
94.652
LAR
94.826
95.608
ASRC
97.087
97.521
LPCC
95.695
95.782
LSF

Figure (4): Identification Rate for the LP based Coefficients
It is clear from Table-4 and its corresponding chart Fig. (4), that all tested LPC-derived
features outperform the raw LPC coefficients which give about 84% identification rates.
4.2. Preemphasis of Speech Signals
There is a need to see the effects of preemphasis on digital speech signals before any
further preprocessing steps. This is obviously demonstrated in the classification results of Fig.
(5) according to the following conditions: preemphasis of speech signals, P = 15, remove DC
offset, no overlap between successive frames, normalization, rectangular window type, and
City-block distance measure were used.

Figure (5): Effect of Preemphasis Speech Samples on Identification Rates
Figure (5) clearly indicates the higher improvements in identification rates overall LPCbased systems in the range of 93% to 98% after applying the preemphasis step to the speech
signal.
79


4.3. LPC Predictor Order (P)
The order of the linear prediction analysis (P) is a compromise among spectral accuracy
and computation complexity (time/memory). Based on the previous tests; further improvements
in identification rates, shown by Table-5 and Fig. (6), can be achieved when LPC predictor
order (P) is studied according to different values (P = 15, 30, 45) with overlapping successive
frames to 50% of frame size.
Table-5: Identification Rates for different LPC Predictor Order (P =15, 30, 45)
P = 15
P = 30
P = 45
92.695
96.347
97.565
LPC
97.652
99.347
99.869
PARCOR
95.478
98.782
99.347
LAR
97.434
99.173
99.695
ASRC
99.130
99.956
99.956
LPCC
98.130
99.695
99.826
LSF

100
LPC
PARCOR
LAR
ASRC
LPCC
LSF

Identification Rate %

99
98
97
96
95
94
93
92
15

30

45

LPC Order P (Number of Coefficients)

Figure (6): Effect of LPC Predictor Order (P =15, 30, 45) on Identification Rates
It is clearly seen from the results of Table-5 and Fig. (6) that the increasing number of
predictor order P with the overlap between successive frames give positive influence for most
identification rates. Therefore, the predictor order P is taken to be 45 for the next experimental
tests.
4.4. Windowing Function
After determining the appropriate LPC predictor order, the system behavior for
different window types must be studied. Therefore, Table-6 is considered for this purpose
according to the following conditions: Rectangular, Hamming and Kaiser window types,
overlap successive frames to 50% of frame size, LPCC cepstral weighting.
From this experiment, it is clearly indicated that the speaker identification rates are
improved further by adopting new window types like Kaiser window. The latter gives the best
accuracy when compared to the other two window types used (rectangular and Hamming).

80


Table-6: Identification Rates for LP based Coefficients with different Window Type
Rectangular Hamming Kaiser
97.5652
94.9130
97.6087
LPC
99.8696
99.4783
99.7391
PARCOR
99.3478
99.4783
99.6087
LAR
99.6957
99.4783
99.6522
ASRC
99.9565
99.9565
100
LPCC
99.8261
99.9130
99.9130
LSF
4.5. Text-Prompt Speaker Verification
Another test is needed for the sake of verifying speakers identity from a randomly textprompt generated by the system. This is relies on the best results obtained from the previous
experiments, it is undoubtedly illustrated that LPCC exhibits paramount results when compared
to other LP based coefficients. Therefore, it is selected to be the feature extraction method for
speaker verification mode. The advantage of text-prompting is that a possible intruder cannot
know beforehand what the phrase will be because the prompt text is changed on each trial.
Furthermore, our system takes additional precautions for the recording time by forcing the user
to utter the pass phrase within a short time interval (up to 15 seconds), which provides
additional difficulty on the intruder to use a device or software that synthesizes the user’s
voice. It is worthwhile that the system is automatically split the sentence back to its attribute
samples, then a pattern matching process is performed only to those equivalent samples
features in the database (with regards to the system security threshold).
A total of 213 speakers’ trials (8 samples length each) from 23 persons (authorized and
unauthorized) are considered for verification test to obtain the optimum threshold of Crossover
Error Rate (CER). This is defined as the point where the False Rejection Rate (FRR) and the
False Acceptance Rate (FAR) curves meet in verifying user's identity. Different threshold
values were considered in the verification test, as shown in Table-7.
Table-7: Text-Prompt Speaker Verification Rates for different Thresholds using Cityblock distance
Threshold Successful
FAR
FRR
(θ)
Decision
15.05
65.2582
0.0000
34.7418
15.40
71.8310
0.0000
28.1690
15.75
83.5681
0.0000
16.4319
16.10
92.9578
0.0000
7.0422
16.45
97.1831
0.0000
2.8169
16.80
99.5305
0.0000
0.4695
17.15
99.5305
0.0000
0.4695
17.50
97.1831
2.8169
0.0000
17.85
96.2442
3.7558
0.0000
18.20
95.3052
4.6948
0.0000
18.55
95.3052
4.6948
0.0000
The successful decision in Table-7 corresponds to the rate of accepting registered
persons and rejecting non-registered ones for all trials. The variation of FAR and FRR with
81


different threshold values are also shown in Fig. (7), where the obtained CER is approximately
17.15 (which is the most suitable security threshold) for 99.53% successful decision rate.

Figure (7): FAR and FRR Performance Curve for different threshold levels using cityblock distance
V. CONCLUSION
A speaker recognition system for 6304 speech samples is presented that relies on LPCderived features and acceptable results have been obtained.
In the closed-set speaker identification, it is found that all tested LPC-derived features
outperform the raw LPC coefficients where 84% to 97% identification rates are achieved. An
improvement in identification rates with LPC-based systems is obtained in the range of 97% to
99% by applying the preprocessing steps (preemphasis, remove DC offset, frame blocking,
overlap successive frames to 50% of frame size, normalization and windowing) to the speech
signal and increasing the predictor order (P). According to speaker identification tests
performed, one can deduce that LPCC exhibits paramount results when compared to other LPC
based coefficients. However, the accuracy can be further improved by weighting the cepstral
coefficients to obtain identification rates close to 100%.
The open-set speaker verification mode is also presented for 213 trials (randomly textprompt sentences generated by the system) from 23 persons (1704 samples). The obtained
verification rates, greater than 99%, using our proposed system model is considered to be quite
suitable.
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