Efficient remote access system based on decoded and decompressed speech signals

This paper investigates the effect of both decoding and decompression on the Speaker Identification (SI) in a remote access system. The coding and compression processes are used for the communication purpose as a normal action taken for voice communication over Internet or mobile networks. In the proposed system, the speech signal is coded with the Linear Predictive Coding (LPC) technique. Also, the speech signal is compressed using two techniques. The first technique depends on decimation process to compress the signal. The signal can be recovered using inverse solutions. The inverse solutions include maximum entropy and regularized reconstruction. The second technique is the Compressive Sensing (CS) and the speech signal can be reconstructed using linear programming. The coded or compressed speech signal is transmitted into the receiver via a wireless communication channel. At the receiver, the received signal is decoded or decompressed, and then SI is performed on the decoded or decompressed speech signal. The performance of coding and compression techniques is evaluated using some metrics such as Perceptual Evaluation of Speech Quality (PESQ) and Dynamic Time Warping (DTW). The objective of SI is to achieve the security needed for the remote access system, and this security can be increased using coding and compression processes. In the SI system, the feature vectors are captured from different discrete transforms such as Discrete Wavelet Transform (DWT), Discrete Cosine Transform (DCT), and Discrete Sine Transform (DST), besides the time domain. The recognition rate for all transforms is computed to evaluate the performance of the SI system.