Speaker identification using multi-layer perceptron neural networks - Andrada, Annliza Nina dL.; Lerma, Diane Felice N.; Obedencio, Christine Pamela D.G.; Ticlao, Rochelle S. (2000)
Open communication systems and computer networks are now prevalent. Thus, there exists the need for access control and authentication. Passwords are the basic method for system authentication but they pose a number of problems. Simple passwords are almost always readily associated with the owner and are more prone to easy guessing while a complicated password is more prone to diremembering. Writing the password on a pice of paper so as not to forget it allows for more complications. What this paper suggests then is an alternative - use a person's voice to identify him or her and prevent unauthorized use and access to facilities and confidential data. Voice, which is inherent to every individual, can be a realible source of security. In this paper, a system for identifying a person through his voice is proposed. This system can be divided into two phases: the enrollment/training phase and the testing phase. Each phase is further divided into three stages, the first two of which are common to both phases. The first stage is Digital Signal Acquisition where voice samples are acquired and converted to data that can be interpreted by the system. The second stage is Speech Analysis where Feature Extraction is done. This is where information inherent to the voice is extracted and the features used are its Mel-Frequency Cepstral Coefficients (MFCC). The third stage is the Classifier where supervised learning is done through a Multi-Layer Perception (MLP) Neural Network. Inputs to the MLP are the MFCCs obtained from the second stage. In the enrollment phase, the MFCCs obtained from all the speakers in the speaker set are presented to the network. In the testing phase, the network is simulated and the extracted features from a recent voice sample are used to identify the speaker.
Thesis (B.S. Computer Science)--University of the Philippines, Diliman.
Keywords: Signal processing -- Digital techniques; Automatic speech recognition; Mel-Frequency Cepstral Coefficients (MFCC).; Multi-Layer Perception (MLP) Neural Network