Activation or Security of machine using Voice Recognition System by MFCC
Lalita A. Palange, Peter Šagát, Shashikant S. Hippargi · 2014
The aim of this thesis is to show the accuracy and time results of a text independent automatic speaker recognition (ASR) system, based on Mel-Frequency Cepstrum Coefficients (MFCC) and Gaussian Mixture Models (GMM), in order to develop a security control access gate. 450 speakers were randomly extracted from the audio data- base, their utterances have been improved using spectral subtraction, then MFCC were extracted and these coefficients were statistically analyzed by GMM in order to build each profile. For each speaker two different speech files were used: the first one to build the profile database, the second one to test the system performance. The accuracy achieved by the proposed approach is greater than 96% and the time spent for a single test run, implemented in Matlab language, is about 2 seconds on a common PC. its conformation can be altered significantly by the movement of the jaw, lips and tongue. The pharyngeal cavity extends to the bottom of the throat and it can be compressed retracting the base of the tongue towards of the wall of the pharynx. In the lower part it ends with the vocal cords: a couple of fleshy membranes traversed by the air coming from the lungs. During the production of a sound, the space between the membranes (glottis) can be completely opened or partially closed. Due to the peculiarity of the voice formation apparatus, it can be possible to recognize a particular individual from its voice. Speaker recognition is classified as a hybrid biometric recognition approach, as it has two components: the physical one related to the anatomy of the vocal apparatus, and the behavioral component, pertinent to the mood of the speaker just in the recording moment.