SPEAKER VERIFICATION SYSTEM
THROUGH TELEPHONE CHANNEL
An Integrated System for Telephony Plataform Asterisk
Alexandre Maciel, Weber Campos, Clêunio França and Edson Carvalho
Informatics Center, Federal University of Pernambuco, Recife, Brazil
Keywords: Speaker Verification, Gaussian Mixture Models, Asterisk.
Abstract: Presented in this work a system of speaker verification based in GMM approach to integration with Asterisk
telephony platforms. Has developed a database itself with 30 speakers and tested for acceptance and
rejection. The results were effective with success rates of 90%.
1 INTRODUCTION
The interest in using speech to verify speaker
identity has increased considerably. This occurs
because two factors: First, speech, opposed to others
biometrics such as fingerprints and face recognition,
allows recognition to be performed remotely as it
can easily transmitted over communication channels.
Second factor is linked to operation’s quantity, value
and complexity increase performed by call center
systems in last years.
The speaker verification systems development
through telephone channel has been widely studied
in academy for a long time. Questions like
microphone variability, transmission noise and
environment are studied in (Naik, 1989) and (Mak,
2002) and several solutions presented with success.
However, the speaker verification systems
integration with telephony platforms is little studied
yet. One reason is the proprietary technologies used
in most of systems.
In this paper we describe a speaker verification
system based on GMM (Gaussian Mixture Models)
approach for telephony environment integrated with
Asterisk. A speaker database was created and false
acceptance rate and false rejection rate were
measured for 8, 16 and 32 gaussian mixtures. O
paper is organized as follows: Section 2 shows
Asterisk description and architecture overview,
section 3 shows speaker verification fundamentals,
section 4 shows the experiments performed. Finally
section 5 brings the authors conclusions and
considerations.
2 ASTERISK
Asterisk is the world’s leading open source
telephony engine and toolkit. Offering flexibility
unheard of in the world of proprietary
communications, Asterisk empowers developers and
integrators to create advanced communication
solutions for free.
Asterisk offers resources like: voice mail,
conference, IVR, and automatic call distribution.
Originally designed for Linux, Asterisk also runs on
different operating systems including NetBSD,
OpenBSD, FreeBSD, Mac OS X, and Solaris. A port
to Microsoft Windows is known as AsteriskWin32.
Asterisk's architecture is very simple. Essentially
Asterisk acts as middleware, connecting telephony
technologies on the bottom to telephony applications
on top, creating a consistent environment for
deploying a mixed telephony environment. More
information about the architecture can be found at
(Meggelen, 2007).
Asterisk's core contains several engines that each
plays a critical role in the software's operation. The
Asterisk Gateway Interface, or AGI, provides a
standard interface by which external programs may
control the Asterisk dialplan. Usually, AGI scripts
are used to do advanced logic, communicate with
relational databases and access other external
resources. For Java applications, the easiest way to
interact with Asterisk is via the FastAGI protocol.
FastAGI basically provides a container that receives
connections from the Asterisk server, parses the
request and calls for scripts mapped to the called
90
Maciel A., Campos W., França C. and Carvalho E. (2009).
SPEAKER VERIFICATION SYSTEM THROUGH TELEPHONE CHANNEL - An Integrated System for Telephony Plataform Asterisk.
In Proceedings of the International Conference on Signal Processing and Multimedia Applications, pages 90-93
DOI: 10.5220/0002237900900093
Copyright
c
 SciTePress

URL (Spencer, 2003).
3 SPEAKER RECOGNITION
Speaker recognition is a biometric modality that uses
individual’s voice for recognize their identity. It is a
different technology from speech recognition, which
recognizes words as they are articulated, which is
not biometrics.
The speech signal is produced as a result of a lot
of changes that occur at different levels: semantic,
linguistic, articulatory and acoustic. The differences
in these transformations appear as differences in
acoustic properties of speech signal. Differences
related to the speakers are the result of a
combination of anatomical differences inherent in
the vocal tract (inherent characteristics) and those
related to the dynamic movement of the vocal tract,
ie how the person speaking (educated
characteristics). In speaker recognition, all these
differences can be used to discriminate between the
speakers themselves (Campbell, 1997).
A speaker recognition system consists of mainly
two parts as shown in Figure 1. In the front-end is
the feature generation part and in the back-end is the
classification engine. During the enrollment phase
the switch is turned towards the upper route and the
system generates models, and during testing or
evaluation phase the models are used to check a
speaker identity from an input speech signal
(Mashao, 2006).
Figure 1: Speaker Recognition System.
3.1 Front-End Processing
Several processing steps occur in the front-end
analysis. First, the speech signal is acquired in the
time domain via sampling and after the application
of the discrete Fourier transform (DFT) it is
converted into the frequency domain. Then is
applied a log magnitude (taking the logarithms of
the magnitude of a complex signal) of the frequency
domain signal (the spectrum) is computed. The
signal is now said to be in the cepstral (a play on the
words spectral) domain.
The signal in the cepstral domain is measured in
quefrencies (again a play on the words frequency).
The low-order quefrencies contain information that
is due to the speech formants and therefore carries
information about what is being said and the high
quefrencies are due to the pitch and therefore
assumed to be speaker dependent.
To obtain the MFC coefficients, the speech
signal is windowed and converted into the frequency
domain by using the DFT. In the frequency domain
a log magnitude of the complex signal is obtained. A
mel-scaling or mel-warping is then performed using
filters. The common method of implementing these
filters is to use triangular filters that are linear
spaced from 0 to 1 kHz and then non-linearly placed
according to the mel-scaling approximations.
The MFCC coefficient has been used for several
years since the late 1990s and has successfully
replaced the linear prediction cepstral coefficients
(LPCC). The main advantage of the LPCC was
computation but with increasing computing power
and better performance (and the use of the fast
Fourier transform-FFT), the MFCC has completely
dominated the designs of the front-ends of speech
technology systems (Mashao, 2006).
3.2 Classification Engine
For many years researches on classification engine
for speech recognition has been done and some of
them have reached high performance level. Many
techniques have been proposed including dynamic
time wrapping (DTW), Hidden Markov models
(HMM), artificial neural networks (ANN) vector
quantization (VQ) and Gaussian Mixture Models
(GMM) (Fechine, 2000).
GMM has been used to characterize speaker’s
voice in the form of probabilistic model. It has been
reported that the GMM approach outperforms other
classical methods for text-independent speaker
recognition (Reynolds, 1995) and other works has
shown that the GMM also performs well for text-
dependent speaker recognition (Reynolds, 2000).
For a feature vector denoted as x
t
belonging to a
specific speaker s, the GMM is a linear combination
of K Gaussian components as follows:
where P(x
t
|m
s,k
,Ʃ
s,k
) is a Gaussian component
parameterized by a mean vector m
s,k
and covariance
matrix Ʃ
s,k
.ω
s,k
is a linear combination coefficient for
speaker s (s=1, 2,…,S). Usually, a diagonal
covariance matrix is used in Eq. (1). Given a
sequence of feature vectors, {x1, x2, …, x
t
}, from a
P(x
t
|λ
s
) =
Ʃ
k=1
K
ω
s,k
P(x
t
|m
s,k
,Ʃ
s,k
),
Generate
Models
Classification
Engine
Speaker
Identified
features
vectors
input
speech
Feature
Generation
(1)
SPEAKER VERIFICATION SYSTEM THROUGH TELEPHONE CHANNEL - An Integrated System for Telephony
Plataform Asterisk
91

specific speaker’s utterances, parameter estimation
for λ
s
= (ω
s,k
, m
s,k
, Ʃ
s,k
) (k =1,…, K; s =1,…,S) is
performed by the Expectation–Maximization (EM)
algorithm. Thus, a specific speaker model is built
through finding proper parameters in the GMM
based on the speaker’s own feature vectors.
4 EXPERIMENTS
4.1 Speakers Database
The experiments described in this section were
conducted with the objective of integrating the
Asterisk telephony platform to an application for
verification of speaker using the GMM approach.
With this propose, it was developed a database with
a small amount of samples for training and testing.
Chosen by a small database for the system were the
closest to a real-time application where users do not
have the time or patience for lengthy recordings.
The database was recorded and processed by the
team and it is composed of 30 speakers (24 men and
6 women) with ages from 20 to 30 years. The
phrases are composed of the first and last name of
each speaker spoken in Portuguese, with 10 true
phrases and 5 false phrases. The recording was
performed in a quiet environment, with fixed phone.
The recorded material was stored in wav mono, 16
bits. Table 1 summarizes the information from
database.
Table 1: Speakers Data Base Description.
Speakers 30 (24 males/6 females)
Section/Speaker 1
Content Name and Last name
Channel Phone or mobile-phone
Acoustic Environment Various
Idiom Portuguese
Audio Sample Size 16 bits
Audio Sample Rate 16 KHz
Format file Wave
4.2 Feature Extraction
The feature extraction process began applying a
voice activity detection algorithm used to discard
silence–noise frames. The speech activity detector is
a self-normalizing, energy based detector that tracks
the noise floor of the signal and can adapt to
changing noise conditions.
Next, the speech was segmented into frames by a
20-ms window progressing at a 10-ms frame rate
and a mel-scale cepstral feature vectors was
extracted from the speech frames. The mel-scale
cepstrum is the discrete cosine transform of the
logspectral energies of the speech segment.
The features consists of the widely used 12 mel-
frequency cepstral coefficients (MFCCs) using C0
as the energy component, appended with delta and
acceleration coefficients, and computed every 10
milliseconds (i.e., 10 ms is the frame shift) for a
frame of 20 ms.
4.3 Threashold
Speaker verification means making a decision on
whether to accept or to reject a speaker. To decide, a
threshold is used with each client speaker. If the
unknown speaker’s maximum probability score
exceeds this threshold, then the unknown speaker is
verified to be the client speaker (i.e., speaker
accepted). However, if the unknown speaker’s
maximum probability score is lower than this
threshold, then the unknown speaker is rejected. The
relationship is shown in Figure 2.
Figure 2: Speaker Verification System.
The threshold is determined as follows:
1. For each speaker, evaluate all samples spoken by
him using his own GMM models and find the
probability scores. From the scores, find the mean,
μ
1
, and standard deviation, σ
1
, of the distribution.
2. For each speaker, evaluate all samples spoken by
a large number of impostors (typically over 20)
using the speaker’s HMM models and find the
probability scores. From the scores, find the mean μ
2
and standard deviation σ
2
of the distribution.
3. For each speaker, calculate the threshold as Eq. 2:
4.4 Classification
The process of classification began with the division
of the database in two different bases for training
and testing. The choice of training vectors was made
randomly among the 10 training phrases. There was
a case of 20 iterations in which 5 phrases were
chosen randomly for training and the other 5 for
testing. This ensured good variability and
consistency of the model reference.
μ
1
σ
2
+
μ
2
2
σ
1
σ
1
+ σ
2
T
=
ID
Speaker
Reject
Speaker
Accept
Score >
Threshold
Unknown
Speaker
yes
no
(2)
SIGMAP 2009 - International Conference on Signal Processing and Multimedia Applications
92

Then the next step was calculating the threshold.
As the reference models were generated for each
speaker they were tested on a validation for
calculating the average distance between true
speakers and impostors. This measurement is stored
to serve as the calculation of decision of the stage of
testing.
The tests database was divided into two groups.
With a genuine phrases (the 5 other phrases of the
stage of training) and another with the phrases of
impostors (5 separate impostors). The process of
testing was also performed in 20 interactions for 16,
32 and 64 mixtures. For both groups were estimated
parameters of Maximum Likelihood.
The result of these distortions was compared to
the threshold calculated in training step and the basic
measures of error used in the system were the False
Acceptance Rate (FAR) and False Rejection Rate
(FRR) as defined below.
Overall performance can be obtained by combining
these two errors into total success rate (TSR) where:
4.5 Results
Table 2 shows a summary of the results of the use of
speaker verification. Have seen the results for 16, 32
and 64 and the results were mixed with 64 blends
the best as expected. The overall success rate (TSR)
was up 91.12%. For the false acceptance rate (FAR)
and false rejection (FRR), the results were very
similar, with no great increase in performance.
Table 2: Speakers Data Base Description.
Mixtures FAR FRR TSR
16 11,23 8,92 89,92%
32 10,98 8,47 90,27%
64 9,43 8,32 91,12%
5 CONCLUSIONS
This article show a speaker verification system
based on the GMM approach to telephony
environments. This system was integrated with the
Asterisk telephony platform and the results are very
similar to systems for quiet environments.
The tests using the GMM approach results in the
range of 90%. Other techniques being worked see
better results in the literature, however, our idea was
to show the ease of integration between the platform
and the asterisk of speaker verification system.
As future work we test in more noisy acoustic
environments with cell phones, make changes in the
extraction of features to achieve more representative
parameters and finally implement the changes as the
GMM as FGMM (Tran, 1998) and Type-2-Fuzzy
GMM - (Zeng, 2007). Thus we want to achieve
better recognition rates and robustness to noisy
environments.
REFERENCES
Naik, J.M., Netsch, L.P., Doddington, G.R. 1989.
In International Conference on Acoustics, Speech, and
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Mak, M.W., Kung, S.Y. 2002. In International
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telephone-based speaker verification.
Meggelen, J. V., Madsen, L., Smith, J., 2007. Asterisk –
The Future of Telephony, O’REILLY. USA, 2
nd
edition.
Spencer, M., et al., 2003. The Asterisk Handbook, Digium,
Inc. USA, 2
nd
version.
Campbell, J.P. 1997. In Proceedings of IEEE, Speaker
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Mashao, D., Skosan, M. 2006. In Pattern Recognition,
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Identification.
Fechine, J.M. 2002. In PhD Thesis, UFCG-Brazil.
Reconhecimento Automático de Identidade Vocal
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Reynolds D., R. Rose. 1995. In IEEE Trans. Speech Audio
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Reynolds, D., T. Quatieri, R. Dunn. 2000. In Digital
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Tran, D., VanLe, T., Wagner M., 1998. In: Proceedings
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speaker recognition.
Zeng, J., Xie, L., Liu, Z.Q., 2007. In Pattern Recognition,
Type-2-Fuzzy Gaussian Mixture Models.
TSR =
FAR + FRR
Total number of accesses
100% - x 100
(
)
Number of re
j
ected
g
enuine claims
Total number of
g
enuine accesses
FRR =
x 100
Number of acce
p
ted im
p
oster claims
Total number of im
p
oster accesses
FAR =
x 100
SPEAKER VERIFICATION SYSTEM THROUGH TELEPHONE CHANNEL - An Integrated System for Telephony
Plataform Asterisk
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