Efficient Remote Access System Based on Coded Speech Signals

Shawky, Hala; Abd-Elnaby, Mohamed; Rihan, Mohamed; Nassar, Mohamed; El-Fishawy, Adel; Abd El-Samie, Fathi

doi:10.21608/mjeer.2019.62761

Efficient Remote Access System Based on Coded Speech Signals

Document Type : Original Article

Authors

Department of Electronics and Electrical Communication, faculty of electric Engineering, Menoufia University, Egypt

10.21608/mjeer.2019.62761

Abstract

This paper investigates an approach for speaker identification in a remote access system based on coded speech signals. The aim of using the coding process is to decrease the amount of transmitted data via the channel. In this proposed system, the speech signal is coded by two different coding techniques. It can be coded either by linear predictive coding or compressive sensing. The coded speech signal is transmitted into the receiver via the wireless communication channel. At the receiver, the received signal is decoded, and then speaker identification system is applied on the decoded signal. During the transmission process, the channel errors affect on the transmitted signal, so they should be taken into account. The speaker identification process is used to achieve the security needed for the remote access system. In speaker identification system, the feature vectors are captured from different discrete transforms such as discrete wavelet transform, discrete cosine transform, and discrete sine transform, besides the time domain. The recognition rate for all transforms is computed to evaluate the effect of coded signals on the performance of the speaker identification system. The results proved that the discrete cosine transform and discrete wavelet transform are the best. In addition to the proposed system gives close recognition results to those obtained from real speech signals revealing a simple degradation effect due to the speech coding.

References

"> [1] KANT, R. a review on low bit rate speech coding.
[2] Bradbury, J. (2000). Linear predictive coding. Mc G. Hill.
[3] Fornasier, M., & Rauhut, H. (2011). Compressive sensing. In Handbook
of mathematical methods in imaging (pp. 187-228). Springer New York.
[4] Srivastava, S. (1999). Fundamentals of linear prediction. Department for
Electrical and Computer Engineering Mississippi State University.
[5] Qaisar, S., Bilal, R. M., Iqbal, W., Naureen, M., & Lee, S. Compressive
Sensing: From Theory to Applications, A Survey.
[6] Vaidyanathan, P. P. (2007). The theory of linear prediction. Synthesis
lectures on signal processing, 2(1), 1-184.
[7] Kutyniok, G. (2013). Theory and applications of compressed
sensing. GAMM‐Mitteilungen, 36(1), 79-101.
[8] Kasacavage, V. (Ed.). (2002). Complete book of remote access:
connectivity and security. CRC Press.
[9] Honda, M. (2003). Human speech production mechanisms. NTT Technical
Review, 1(2), 24-29.
[10] De Rango, F., Tropea, M., Fazio, P., & Marano, S. (2006). Overview on
VoIP: Subjective and objective measurement methods. International
Journal of Computer Science and Network Security, 6(1), 140-153.
[11] Bachu, R. G., Kopparthi, S., Adapa, B., & Barkana, B. D. (2008, June).
Separation of voiced and unvoiced using zero crossing rate and energy of
the speech signal. In American Society for Engineering Education (ASEE)
Zone Conference Proceedings (pp. 1-7).
[12] Peleg, N. (2009). Linear Prediction Coding. Update, 1.
[13] Vaseghi, S. V. (1996). Linear prediction models. In Advanced Signal
Processing and Digital Noise Reduction (pp. 185-213). Vieweg+ Teubner
Verlag.
[14] Cinneide, A. O. (2008). Linear prediction: The technique, its solution and
application to speech. Dublin Institute of technology.
[15] Taylor, P. (2009). Text-to-speech synthesis. Cambridge university press. 16] Madane, A. R., Shah, Z., Shah, R., & Thakur, S. (2009). Speech
compression using Linear predictive coding. In Proceeding of the
International Workshop on Machine Intelligence Research (pp. 119-21).
[17] Ahmed, S. (2011). Compressive Sensing for Speech Signals in Mobile
Systems.
[18] Baraniuk, R. G. (2007). Compressive sensing [lecture notes]. IEEE signal
processing magazine, 24(4), 118-121.
[19] Desai, S., & Nakrani, N. (2013). Compressive sensing in speech
processing: A survey based on sparsity and sensing matrix. International
Journal of Emerging Technology and Advanced Engineering, 3(12), 18-23.
[20] Al-Azawie, M. K., & Gaze, A. M. (2016). Combined speech compression
and encryption using contourlet transform and compressive
sensing. International Journal of computer applications, 140(5), 6-10.
[21] Pooja C.Nahar, Dr.Mahesh T.Kolte. (2014). An introduction to
compressive sensing and its applications. International journal of specific
and research publications.
[22] Ding, X., Chen, W., & Wassell, I. J. (2017). Joint sensing matrix and
sparsifying dictionary optimization for tensor compressive sensing. IEEE
Transactions on Signal Processing, 65(14), 3632-3646.
[23] El-Samie, F. E. A. (2011). Information security for automatic speaker
identification. In Information Security for Automatic Speaker
Identification (pp. 1-122). Springer New York.