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Pre-emphasis, Windowing and Spectral Estimation of Silent Speech Signals Using Embedded Systems
Leonardo A. Góngora,Dario Amaya,Olga L. Ramos 보안공학연구지원센터 2016 International Journal of Multimedia and Ubiquitous Vol.11 No.10
The feature extraction process is the fundamental stage in the development of speech processing systems. In this paper is described the methodology and implementation of an embedded algorithm for extracting characteristic information from silent speech signals. The classical approach based on frequency representations of the signals is followed as methodology for this work. First, the acquisition stage of the silent speech signals is performed using a Non-Audible Murmur microphone and the STM32F4Discovery evaluation board. Then, the digitalized signal is filtered, segmented and normalized using the pre-emphasis and windowing steps. The magnitude spectrogram is calculated from the pre-processed signal using the Fast Fourier Transform (FFT), to finally estimate characteristic data from de silent speech signal. As result of this process, the signal characteristic parameters, defined in the frequency domain are obtained and used as elements at later stages of pattern recognition, in order to build systems of Automatic Speech Recognition (ASR), Speech Coding, Speaker Recognition, among other applications.