A Speech Scrambler Algorithm Based on chaotic system

Al- Mustansiriyah J. Sci.

Vol. 24, No 5, 2013

A Speech Scrambler Algorithm Based on chaotic system Saad N.Al-saad and Eman H.Hashim Al-Mustansiriyah University/ College of Science/ Computer Science Dept. Received 3/4/2013 – Accepted 15/9/2013

‫الخالصة‬ ‫ تتكون الخوارزمية من جزئين من عمليات‬.‫يقدم البحث خوارزمية آمنة لبعثرة أشارة الكالم‬ ‫) البدال المعامالت‬logistic( ‫ يتم في الجزء االول توليد مفتاح ابدال باستخدام خرائط‬.‫االبدال‬ ‫ عمليه االبدال الثانية تتولد من تطبيق خريطة‬.‫الناتجة من التحويل المويجي المتقطع‬ ‫ نتائج المحاكاة التي قدمت في هذا البحث‬.)Arnold cat map( ‫) ذات البعدين نوع‬chaotic( ‫تشير الى أن الخوارزمية تقدم كالم مبعثر غير مفهوم وكالم مسترجع بعد التشفير ذو نوعية‬ .‫جيدة‬

ABSTRACT This paper presents a secure scramble speech signal algorithm. The algorithm composed of two types of permutations. The first permutation key is generated from two logistic maps to permute the coefficients resulting from Discrete Wavelet Transform (DWT). The second permutation is generated from performing two dimensional chaotic map using Arnold cat map. Simulation results presented in the paper indicate that the algorithm provides scrambled speech of low residual intelligibility and good quality recovered speech.

1. INTRODUCTION Speech encryption techniques are used to encrypt clear speech into an unintelligible signal in order to avoid eavesdropping. Speech has more redundancy as compared with written text or digital data and contains two types of information, the content of the speech and the personality of the speaker. This makes encryption of speech signal with low residual intelligibility and high cryptanalytic strength is very difficult task [1, 2]. In general there are two basic speech encryption modes: digital and analog [1]. Digital encryption is cryptanalytic strength and retains a lower residual intelligibility, but it needs complex implementation and produce low quality recovered speech. On other hand, analog speech encryption, also called speech scrambling, acts on the speech samples themselves. Analog encryption schemes are relatively less secure compared to digital encryption schemes, but have an advantage of less complexity and provide good quality of recovered speech [1]. In general, there are five main categories domains in analog speech encryption: frequency-domain, time-domain, amplitude, twodimensional scrambling that combines the frequency-domain scrambling with the time-domain scrambling and transform domain [1]. Regarding other types of scramblers which can attain a high degree of security, the transform domain scrambler has advantage in that the number of effective permutations is much larger than the number of permutations available in other domain scrambling algorithms.

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Many analog speech encryption methods in the transform domain are proposed, e.g., Fast Fourier Transform, a Prolate Spheroidal Transform (PST), Hadamard Transform domain, circulant transform domains, discrete wavelet transform domain (DWT) and discrete cosine transform (DCT). Among the transform-domain techniques, DCT and DWT have proved to be the best for speech encryption[2]. One interesting new speech encryption methods is connected to chaos theory. That theory focuses primarily on the description of these systems that are often very simple to define, but whose dynamics appears to be very confused. Indeed, chaotic systems are characterized by their high sensitivity to initial conditions and pseudo-random behavior. The extreme sensitivity to the initial conditions (i.e. a small deviation in the input can cause a large variation in the output) makes chaotic system very attractive for pseudo-random number generators [3]. It is impossible to predict the behavior of the chaotic system even if we have partial knowledge of its organization that made chaotic system [4]. In this paper chaotic map is used to produce the chaotic sequence by logistic map and samples are permutated using Arnold cat map. 2. Wavelet Transform Scrambling Process Transform of a signal is just another form of representation to the signal. It does not change the information content present in a signal. The Wavelet Transform provides a time – frequency representation of the signal. It was developed to overcome the short coming of the Short Time Fourier Transform (STFT), which can also be used to analyze non-stationary signals. STFT gives a constant resolution at all frequencies while the Wavelet Transform uses multi-resolution technique by which different frequency is analyzed with different resolutions [5]. The analog scrambling process which employs a transformation of the input speech to facilitate encryption can best be described using matrix algebra. Let us consider the vector x which contains N speech time samples obtained from analog to digital conversion process, representing a frame of the original speech signal. Let this speech sample vector x be subject to an orthogonal transformation matrix F such that [5, 6, 7]: 𝑢=𝐹𝑥 (1) This transformation results in a new vector u made up of N transform coefficients (N is the number of coefficient produce from the transform in frequency domain). A permutation matrix P is applied to u, such that each transform coefficient is moved to a new position within the vector given by [5, 6, 7]: 358

Al- Mustansiriyah J. Sci.

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𝑣=𝑃𝑢 (2) A scrambled speech vector y is obtained by returning vector v to the time domain using the inverse transformation F-1 where [5, 6, 7]: 𝑦 = 𝐹 −1 𝑣 (3) Descrambling, or recovery of the original speech vector x' is achieved by first transforming y back to the transform domain .The inverse permutation matrix P-1 is then used to return the transform coefficients to their original position. Finally, the resulting transform vector is returned to the time domain by multiplying by F-1 [5, 6, 7]: 𝑥̀ = 𝐹 −1 𝑃−1 𝐹 𝑦 (4) The transform domain scrambling process outlined above requires the transform matrix F to have an inverse. One attempts to insure that the scrambling transformation T=F-1PF is orthogonal since orthogonal transformations are norm preserving. The inverse transformation T-1 will also be orthogonal .This property is useful since any noise added to the scrambling signal during transmission will not be enhanced by the descrambling process [5, 6, 7]. 3. Generation of Permutation Key Scheme The prime requirement of any permutation key is that the Residual Intelligibility should be minimized after permutation. So the problem of key generation is therefore an important issue in the design of a scrambling system. High key sensitivity is required by secure cryptosystems, which means that the cipher cannot be decrypted correctly although there is only a slight difference between encryption or decryption keys. Logistic map is one-dimensional linear chaotic map has the advantages of high-level efficiency and simplicity is defined as: 𝑥𝑛+1 = 𝑟. 𝑥𝑛 . (1 − 𝑥) (5) Where xn is an initial condition variable which lies in the interval (0, 1) and r is called control parameter which lies in the interval (1, 4) [8]. The parameter r can be divided into three segments, when r (0, 3) the calculation results come to the same value after several iterations without any chaotic behavior. When r in the interval [3, 3.6), the phase space concludes several points only, while r [3.6, 4), it becomes a chaotic system [8]. The resulting plot that depicts the possible output values for different parameter conditions is called the Bifurcation Diagram, as shown in Figure (1)

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A Speech Scrambler Algorithm Based on chaotic system Saad and Eman

Figure-1:The Bifurcation Diagram [9].

4. Arnold Cat Map Scrambling Arnold Cat Map is used to reduce the autocorrelation samples of speech signal. Arnold cat map is given by: 𝑥𝑛+1 𝑥𝑛 1 𝑎 [𝑦 ] = [ ] . [𝑦 ] 𝑚𝑜𝑑 (𝑁) (6) 𝑏 𝑎𝑏 + 1 𝑛+1 𝑛 Where xn ,yn are the position of samples in the NxN matrix, and xn,yn ∈ {0,1,2,…,N-1} and xn+1,yn+1 are the transformed position after cat map, taking mod in order to bring x, y in unit matrix, a and b are two control parameters and are positive Integers[10].

For applying Arnold cat map, the signal must convert from 1-D vector to 2-D. Matrix resizing operations for both 1-D vector to 2-D matrix and 2-D matrix to 1-D vector is shown in Figure (2).

Figure-2:Matrix converter operations for both 1D vector to 2D matrix and 2D matrix to 1D vector [2]. vector to 2D matrix & 2D matrix to 1D vector [].

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5. Measurements Criteria Generally speech quality assessment falls in two categories: subjective and objective quality measures. Subjective quality measures are based on comparison of original and processed speech data by a listener or a panel of listeners. Objective speech quality measures are based on some physical measurement. Objective speech quality measures can be classified to time domain measures and spectral domain measures. Time domain measures take up speech waveforms directly in time domain(Signal-to-Noise Ratio (SNR), Segmental Signal-to-Noise Ratio (SEGSNR)) while spectral domain measures are computed using speech segment, they are more reliable than the time-domain measures(Log-Likelihood Ratio (LLR) , Cepstrum Distance (CD)) Signal-to-Noise Ratio (SNR) This common measure is given as

SNR = 10log10

2 ∑∞ n=∞ x (n) 2 (dB) ∑∞ n=∞[x(n)−y(n)]

(7)

Where n is the number of samples, x(n) is the input speech signal and y(n) is the reconstructed speech signal. A high SNR (SNR >> 1) indicates high precision data, while a low SNR indicates noise contaminated data [11, 12]. Segmental Signal-to-Noise Ratio (SEGSNR) It is an improved version measure that can be obtained if SNR is measured over short frames. It is given as:

SEGSNR =

10 M

Nm+N−1 ∑M−1 m=0 log10 ∑n=Nm

2 ∑∞ n=∞ x (n) 2 (dB) ∑∞ n=∞[x(n)−y(n)]

(8)

Where M is the number of segments in the output signal, and K is the length of each segment. It is a good estimator for speech signal quality [12]. As depicted in Figure (3), high intelligibility and low intelligibility correspond to high and low number respectively.

Figure-3:Segmental signal-to-noise ratio measure.

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Log-Likelihood Ratio (LLR) The Log-Likelihood Ratio (LLR) measure is a distance measure that can be directly calculated from the LPC vector of the clean and distorted speech. LLR measure can be calculated as follows:

𝑑𝑙𝑙𝑅 (𝑎𝑑 , 𝑎𝑐 ) = 𝑙𝑜𝑔

𝑇 𝑎𝑑 𝑅𝑐 𝑎𝑑

(9)

𝑎𝑐 𝑅𝑐 𝑎𝑐𝑇

Where ac is the LPC vector for the clean speech, ad is the LPC vector for the distorted speech, aT is the transpose of a, and Rc is the autocorrelation matrix for the clean speech [13]. Cepstrum Distance (CD) The Cepstrum Distance (CD) is an estimate of the log-spectrum distance between clean and distorted speech. Cepstrum can also be calculated from LPC parameters with a recursion formula.CD can be calculated as follows:

𝑑𝐶𝐸𝑃 =

10 𝑙𝑜𝑔10

𝑝 √2 ∑𝑘=1{𝑐𝑐 (𝑘 ) − 𝑐𝑑 (𝑘 )}2

(10)

Where cc and cd are Cepstrum vectors for clean and distorted speech, and P is the order (number of LPC coefficient). Cepstrum distance is also a very efficient computation method of log-spectrum distance [13]. As the value of LLR and CD are increasing the low residual intelligibility for scrambled signal (the scrambled signal is farthest to the original speech signal). 6. A Proposed Speech Scrambling algorithm The proposed algorithm can be treated as two major parts: speech scrambling and speech descrambling. Figure (4) shows the steps for each part. The proposed scrambling algorithm steps can be summarized as follows: 1. Segmentation (frames of length N=256 samples per frame) 2. First part • Generation of key permutation (two logistic maps used) • Application of the DWT (Haar DWT). • Permutation the coefficient of DWT with the key permutation. • Application of the Inverse DWT. 3. Second part • convert into 2-D format. • Application Arnold cat map on the samples in time domain. • convert into 1-D format. 4. Synthesis segments and saves to the wave file. The descrambling steps of the proposed algorithm can be summarized as follows: 362

Al- Mustansiriyah J. Sci.

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1. Segmentation (frames of length N=256 samples per frame) 2. First part • convert into 2-D format. • Application inverse Arnold cat map on the samples in time domain • convert into 1-D format. 3. Second part • Generation of key permutation (using same initial condition and control parameters value that used in sender). • Application of the DWT (Haar DWT). • Inverse permutation of the coefficient of DWT with the key permutation. • Application of the Inverse DWT 4. Synthesis segments and saves to the wave file. These steps are taken to be illustrated separately in more details 6.1 Speech Scrambling It is composed of main steps: segmentation, transformation and two types of permutation applied in transform domain and in time domain. Segment and Framing The sampled speech is segmented into frames of length N=256 samples per frame that means a time frame of (32 msec), with sampling frequency of 8 KHz, Mono, 16 bit resolution. The speech files used for scrambling purposes are taken from the web page http://www.1speechsoft.com/voices.html as wave files [14]. Input Speech Signal

Segmen t and Framin g

Discrete Wavelet Transform

Permutation DWT coefficients

Inverse Discrete Wavelet Transfor m

Arnold cat map permutation

Permutation key generated from pair of logistic maps

Output Speech Signal

Synthesis segments

Inverse Discrete Wavelet Transform

Inverse Permutatio n DWT coefficients

Channel

Discrete Wavelet Transform

Figure-4:A Proposed Speech Scrambling algorithm.

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Synthesis Scramblin g Signal

Inverse Arnold Cat Map Permutation

Segment Scramblin g Signal

A Speech Scrambler Algorithm Based on chaotic system Saad and Eman

Discrete Wavelet Transform A wavelet transform of type (Haar) is chosen and performed on each frame with specified level (two levels of decomposition). The result is transforming coefficients. The Permutation Key The wavelet coefficients are permuted using the following procedure: Choosing two logistic maps and cross-coupled as shown in the Figure (5). The output generated by the first logistic map is fed to the second logistic map as the input (initial condition) and vice versa. The output is two rows of sequence. The first one contains real value representing the chaos sequence. The second row represents the positions of chaos sequence. Finally the chaos sequence is sorted in ascending order. The resultant position row is used as permutation key. Generating permutation key sequence can be summarized as follows: Step1. Generate the chaotic sequence of length n by using two logistic maps and store it in a one dimensional matrix {a1, a2, a3, a4, . . . . . . , an}. Step2. Find the index of the smallest number from the sequence of n number and then store it in b (1). Next find the index of the 2nd smallest number and store it in b (2). Repeat this process until nth smallest number is stored in b (n). Step3. The array b contains sequence of n random number generated from the chaos sequence.

Figure-5: Generation of permutation key

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Permutation using Arnold Cat Map The sample in time domain are converted in to 2-D matrix of samples, and then permuted by multiplying by Arnold cat map matrix, the output is resized to 1-D vector again. Synthesize the scrambling frames produced from Arnold cat map permutation part and the resulting scrambled speech signal was saved as a wave file. 6-2 Speech Descrambling After transmission through the channel, the receiver receives the scrambled speech. The receiver segment the received signal to be ready to descramble it. Inverse Permutation using Arnold Cat Map First the matrix of sample is resized to 2-D and multiplied by the inverse Arnold cat map matrix, the output is then resized to 1-D vector to be the input to the DWT step. Descrambling using DWT Applying the DWT used to generate wavelet coefficients. These coefficients are rearranged using inverse permutations (that means recovering of the original order of every frame), then the inverse wavelet transform is applied to transform the signal in to time domain. Synthesize the frames to present the recovered speech (descrambled speech signal) and save in wave file. 7. Simulation Results Subjective Test is considered by playing the scrambled speech files back to a number of listeners to measure the residual intelligibility. The judge is that; the listened files contain noise only, which means that the residual intelligibility is low. The analog recovered speech files have been tested in a similar way to measure the quality of the recovered speech files; the judge is that the files are the same as the original copies. The key permutation is computed using pair of logistic maps with initial conditions (x1=0.41) and control parameters (r1=3.94 and r2=3.98). That key is used to permute the DWT coefficient. As the chaotic system are dynamic systems, which are very much sensitive to the initial condition and control parameter, thus, a small variation to the control parameters( seed ) creates a major impact on the scrambled signal. This effect can be viewed in the simulation example (key length =25) by changing the r1 and r2 value from 3.992 and 3.882 to 3.991 and 3.881 respectively. Table (1) and (2) show the result.

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Table-1: Generation key permutation with (x1=0.65, r1=3.992, r2=3.882) Chaotic sequence control parameter r1=3.992 r2=3.882 0.406189350901439 0.962868651965901 0.142724423220861 0.488437815650636 0.997466333045141 0.0100887719665914 0.0398680586780157 0.152808157528446 0.51679563548866 0.9968738832611 0.0124404457790751 0.0490444389028686 0.186183215006961 0.604863949622878 0.954102179861406 0.174814511298517 0.575863556568406 0.975024925372891 0.097210470535722 0.350340295056151 0.908587075082751 0.331561955484688 0.884741471638152 0.407080208006824 0.963532721725732

Ascending order of chaotic sequence Index 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25

0.0100887719665914 0.0124404457790751 0.0398680586780157 0.0490444389028686 0.097210470535722 0.142724423220861 0.152808157528446 0.174814511298517 0.186183215006961 0.331561955484688 0.350340295056151 0.40618935090143 0.407080208006824 0.488437815650636 0.51679563548866 0.575863556568406 0.604863949622878 0.884741471638152 0.908587075082751 0.954102179861406 0.962868651965901 0.963532721725732 0.975024925372891 0.9968738832611 0.997466333045141

Key permutation 6 11 7 12 19 3 8 16 13 22 20 1 24 4 9 17 14 23 21 15 2 25 18 10 5

Table-2: Generation key permutation with (x1=0.65, r1=3.991, r2=3.881) Chaotic sequence control parameter r1=3.991 r2=3.881 0.892720158268834 0.382221571261605 0.942387812720144 0.216683455418336 0.677399356645127 0.872151107833224 0.44501067977757 0.98568191307394 0.0563252995375079 0.212132665836546 0.667025400105211 0.8864111402373 0.401839544925735 0.959294819512916 0.155841640655474 0.525036099559897 0.995248416131838 0.0188734642187522 0.0739023709594386 0.27314727480931 0.792364523724575 0.656611234201804 0.899862428995255 0.359629161171157 0.91911144617749

Ascending order of chaotic sequence Index 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25

0.0188734642187522 0.0563252995375079 0.0739023709594386 0.155841640655474 0.212132665836546 0.216683455418336 0.273147274809314 0.359629161171157 0.382221571261605 0.401839544925735 0.44501067977757 0.525036099559897 0.656611234201804 0.667025400105211 0.677399356645127 0.792364523724575 0.872151107833224 0.8864111402373 0.8927201582688 0.899862428995255 0.91911144617749 0.942387812720144 0.959294819512916 0.985681913073947 0.995248416131838

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Key permutation 18 9 19 15 10 4 20 24 2 13 7 16 22 11 5 21 6 12 1 23 25 3 14 8 17

Al- Mustansiriyah J. Sci.

Vol. 24, No 5, 2013

From above example, we can conclude that the permutation key that used in proposed algorithm is very sensitive to the initial seed that means the scrambled signal cannot be descrambled correctly, if there is tiny change in initial seed. key sensitivity indicate high security and suitability of the proposed algorithm. Objective Test is another valuable measure to the residual intelligibility of the scrambled speech and the quality of the recovered speech. In this paper (SNR), (SEGSNR), (LLR), (CD) measures are calculated for speech signal for two male and two female persons on sentences in English language. The sentence is “This is an example of the AT and T natural voice speech engine, it is the most human sounding text to speech engine in the world”. Table (3) and (4) explains the result. Table-3:Result for comparison of original and scrambled speech signal.

File name Mike8(male) Claire8(female) charles8 (male) lauren8(female)

SNR -2.62555 -2.58369 -2.61635 -2.58401

SEGSNR -2.56744 -2.52513 -2.45871 -2.54969

LLR 4.18696 1.148503 3.78171 2.13405

CD 7.7533 4.63589 8.21462 5.74775

Table-4:Result for comparison of original and descrambled speech signal.

File name Mike8(male) Claire8(female) charles8 (male) lauren8(female)

SNR 13.67320 14.79295 16.52925 13.88546

SEGSNR 62.84561 61.20851 62.022102 62.34254

LLR 0.002382 0.010630 0.00393 0.00100

CD 0.30001 0.61856 0.34891 0.17526

From table (3) the LLR (also called LPC distance) and CD measures for all the scrambled speech files are high while SNR and SEGSNR measures are low (negative value) which means that the residual intelligibility is very low, when compared with [6] that used LLR,CD, and SEGSNR measures for scrambling algorithm using different transform. From table (4) the LLR and CD measures for all the descrambled speech files are low while SNR and SEGSNR measures are high. As the values of the LLR and CD are decreased, and the value of SND and SEGSR are increased that indicates high precision data and good quality of the descrambled speech signals. The Figure (6) shows the waveform for original speech signal, scrambling signal and descrambling signal.

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A Speech Scrambler Algorithm Based on chaotic system Saad and Eman

Orginal Signal 1

Amplitude

0.5 0

-0.5 -1 0

0.5

1

1.5

2

2.5

3

Samples

3.5 x 10

5

a Scrambled Signal

Amplituede

1 0.5 0 -0.5 -1 0

0.5

1

1.5 2 Samples

2.5

3

3.5 5

x 10

b Descrambled Signal 1

Amplitude

0.5 0 -0.5 -1 0

0.5

1

1.5

2 2.5 Samples

3

3.5 5

x 10

c Figure-6:(a) The waveform of original speech signal, (b) Scrambled speech signal, (c) Descrambled speech signal.

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The spectrum and spectrogram plotting is used because it is a powerful tool that allowed to see the different in the frequency and time domains. Note that on the scrambled plot it is observed that the order of the frequencies has changed. And, as expected the descrambled version has been decoded to its original form. The Figures (7) and (8) show spectrum and spectrogram for original speech signal, scrambling signal and descrambling signal. Spectrum of original signal 10

Spectrum

0 -10 -20 -30 -40 -50 0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

0.8

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Frequency

a Spectrum of scrambled signal -10

Spectrum

-15 -20 -25 -30 0

0.1

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0.3

0.4

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Frequency

b Spectrum of descrambled signal 10

Spectrum

0 -10 -20 -30 -40 -50 0

0.1

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C Figure-7:(a) The spectrum of original speech signal, (b) Scrambled speech signal, (c) Descrambled speech signal.

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A Speech Scrambler Algorithm Based on chaotic system Saad and Eman Spectrogram of original signal 1 0.9 0.8

Frequency

0.7 0.6 0.5 0.4 0.3 0.2 0.1 0 0.5

1

1.5

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4

x 10

a Spectrogram of scrambled signal 1

Frequency

0.8

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0.2

0

0.5

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1.5

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2.5

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4

x 10

b Spectrogram of descramled signal 1 0.9 0.8

Frequency

0.7 0.6 0.5 0.4 0.3 0.2 0.1 0

0.5

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1.5

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2.5

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4

x 10

c Figure-8:(a) The spectrogram of original speech signal, (b) Scrambled speech signal, (c) Descrambled speech signal.

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8. CONCLUSIONS This paper presents new idea for speech scrambling algorithm based on wavelet transforms domain and chaotic system. The performance of proposed algorithm is examined on actual English Speech Signals, and the results showed that there is low residual intelligibility in the scrambled speech signal while preserving the quality of the reconstructed speech signal. Using two parts to scramble the speech signal; the first part works with time–frequency features and the second part works on time features with advantage of chaotic system (Sensitivity to initial conditions, Topological transitivity with iterative process) make the system difficult to decrypt that means it is crypt analytically strong and secure. 9. REFERENCES 1. A.Srinivasan1 P.Arul Selvan, “A Review of Analog Audio Scrambling Methods for Residual Intelligibility”, Innovative Systems Design and Engineering, Vol 3, No 7, 2012. 2. L. A. Abdul-Rahaim, “Proposed Realization of modified Scrambling using 2D-DWT Based OFDM Transceivers “,MASAUM Journal of Computing, Volume 1 Issue 2, September 2009. 3. M. Francois and D. Defour, “A Pseudo-Random Bit Generator Using Three Chaotic Logistic Maps “, hal-00785380, version 1 - 6 Feb 2013. 4. M. N. Elsherbeny, M. Rahal, "Pseudo Random Number Generator Using Deterministic Chaotic System “, International Journal of scientific& technology research, Vol 1, Issue 9, October 2012. 5. S. B. Sadkhan, N. H. Kaghed, L. M. AlSaidi, “Design and Evaluation of Transform Based Speech Scramblers using different Wavelet Transformations”, (CSNDSP, Greece, 2006). 6. S.Sridharan, E.Dawson, B.Goldburg, “Design and cryptanalysis of transformbased analog speech scramblers”, IEEE Journal on selected areas in communications , (735744), (June 1993) . 7. D. B.Sadkhan, D. Abdulmuhsen, N. F.Al-Tahan, “A proposed analog speech scrambler based on parallel structure of wavelet transforms”, 24th National Radio Science Conference (NRSC) (C13/1C13/12), (2007). 8. I. Q. Abduljaleel, “Speech Encryption Technique Based on BioChaotic Algorithm”, AL-Mansour Journal, Basra University, No.17/ Special Issue, 2012. 9. A.V. Prabu S. Srinivasarao, Tholada Apparao, M. Jaganmohan Rao and K. Babu Rao, “Audio Encryption in Handsets”, International Journal of Computer Applications, Vol. 40 No.6 (0975 – 8887) (2012). 371

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10.M. Ashtiyani , P. Moradi Birgani, S. S. Karimi Madahi, “Speech Signal Encryption Using Chaotic Symmetric Cryptography”, J. Basic. Appl. Sci. Res., 2(2) (1678-1684) (2012). 11.S. Sadkhan and N. Abbas, “Performance evaluation of speech scrambling methods based on statistical approach”, Attidella “Fodazione Giorgio Ronchi” Anno LXVI, N. 5, (2011). 12. E. Mosa, N. W. Messiha, O. Zahran, F. E. Abd El-Samie, “Chaotic encryption of speech signals”, Springer US, Vol. 14, Issue4,( 285-296), December (2011). 13.K. Kondo, “Subjective Quality Measurement of Speech its Evaluation, Estimation and Applications”, Signals and Communication Technology, Springer US (2012) 14. http://www.1speechsoft.com/voices.html.

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