Support vector machines, Mel-Frequency Cepstral Coefficients and the Discrete Cosine Transform applied on voice based biometric authentication

In this paper, the implementation of a Support Vector Machine (SVM) is proposed based on automatic system for voice biometric authentication, recognizing the speaker using Mel-Frequency Cepstral Coefficients and the Discrete Cosine Transform. The voice recognition problem can be modeled as a classification problem, where the objective is to obtain the best degree of separability between the classes which represent the voice. Building an automated speech recognition system capable of identifying the speaker, has many techniques using artificial intelligence and general classification at disposal, Support Vector Machines being the one used in this work. The voice samples used are in the Brazilian Portuguese language and had its features extracted through the Discrete Cosine Transform. Extracted features are applied on the Mel-frequency Cepstral Coefficients to create a two-dimensional matrix used as input to the SVM algorithm. This algorithm generates the pattern to be recognized, leading to a reliable speaker identification using few parameters and a small dataset.