Efficient Remote Access System Based on Coded Speech Signals | ||||
| Menoufia Journal of Electronic Engineering Research | ||||
| Article 3, Volume 28, Issue 2, July 2019, Page 33-52 | ||||
| Document Type: Original Article | ||||
| DOI: 10.21608/mjeer.2019.62761 | ||||
 View on SCiNiTO | ||||
| Authors | ||||
| Hala Shawky* ; Mohamed Abd-Elnaby; Mohamed Rihan; Mohamed Nassar; Adel El-Fishawy; Fathi Abd El-Samie | ||||
| Department of Electronics and Electrical Communication, faculty of electric Engineering, Menoufia University, Egypt | ||||
| 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. | ||||
|
Statistics Article View: 154 |
||||
