Avaliação de uso do coeficientes mel-cepstrais na representação das características vocais de um locutor.

A identificação de indivíduos por meio de biometria vem sendo bastante usada como mecanismo de segurança para o acesso a sistemas computacionais ou ambientes restritos. Os sistemas biométricos têm sido desenvolvidos para realizar a identificação por impressão digital, iridia ou vocal, por exemplo. Usar a voz como meio para a autenticação individual tem sido cada vez mais possível, devido ao avanço significativo na área de Processamento Digital de Sinais de voz. Esta pesquisa tem como finalidade avaliar a eficiência dos coeficientes mel-cesptrais na representação das características de um locutor em um sistema automático de verificação de locutor. As técnicas utilizadas para a construção do sistema automático de verificação de locutor, visando a uma implementação em hardware, incluem o uso de: (i) coeficientes mel-cepstrais, na composição do vetor de características; (ii) quantização vetorial, na obtenção de padrões; e (iii) uma regra de decisão, baseada na distância Euclidiana. O sistema utilizado para a avaliação da representação das características vocais de um locutor é uma modificação de outro sistema automático de verificação de locutor que utiliza coeficientes LPC para a representação das características vocais de um locutor. Para tanto, fez-se uso das linguagens C++ (fase de treinamento) e SystemVerilog (fase de verificação). Os resultados utilizando coeficientes mel-cepstrais foram de 99,34% na taxa de acerto, 0,17% para taxa de erros e 0,49% na taxa de respostas desconhecidas, comparados, respectivamente, a 96,52% na taxa de acerto, 0,90% para taxa de erros e 2,58% na taxa de desconhecidos para coeficientes LPC.Biometric identification of individuals has been widely used as a security mechanism for accessing computer systems or restricted environments. Biometric systems have been developed to perform identification through fingerprint, iris, or voice, for example. Using the voice as a biometric identifier has been increasingly possible due to significant advances in digital processing of speech signals area. This research aims to evaluate the efficiency of mel-frequency cepstral coefficients in the representation of the characteristics of a speaker in an automatic speaker verification. The techniques used to construct the automatic speaker verification system aiming at a hardware implementation included the use of: (i) melfrequency cepstral coefficients, like feature vector; (ii) vector quantization, in patterning modelling; and (iii) a decision rule, based on Euclidean distance. The system used for evaluation in the representation of the characteristics of a speaker is a modification of another automatic speaker verification system using linear predictive coding coefficients for the representation of the vocal characteristics of a speaker. It was implemented using C++ for the training phase, and SystemVerilog for the verification phase. The results using mel-frequency cepstral coefficients were 99.34% in the hit rate, 0.17% to error rate and 0.49% to unknown response rate, compared respectively to 96.52% in success rate, 0.90% to error rate and 2.58% to unknown rate using the linear predictive coding coefficients.CNP

Speech-based class attendance

In the department of engineering, students are required to fulfil at least 80 percent of class attendance. Conventional method requires student to sign his/her initial on the attendance sheet. However, this method is prone to cheating by having another student signing for their fellow classmate that is absent. We develop our hypothesis according to a verse in the Holy Qur’an (95:4), “We have created men in the best of mould”. Based on the verse, we believe each psychological characteristic of human being is unique and thus, their speech characteristic should be unique. In this paper we present the development of speech biometric-based attendance system. The system requires user’s voice to be installed in the system as trained data and it is saved in the system for registration of the user. The following voice of the user will be the test data in order to verify with the trained data stored in the system. The system uses PSD (Power Spectral Density) and Transition Parameter as the method for feature extraction of the voices. Euclidean and Mahalanobis distances are used in order to verified the user’s voice. For this research, ten subjects of five females and five males were chosen to be tested for the performance of the system. The system performance in term of recognition rate is found to be 60% correct identification of individuals