Autonomous Framework for Person Identification by Analyzing Vocal Sounds and Speech Patterns

Speech processing has emerged as one of the important and crucial domain over the past decade. Many researchers have worked on voice recognition and verification. Some of the reported work has been done in the field of biometrics. However, this paper proposes an autonomous algorithm for the person identification by analyzing their vocal sounds and speech patterns. First, the input voice signal is introduced to our proposed system from which the low frequency contents are extracted using finite response low pass filter based on hamming window. Then the proposed system performs a cepstral analysis and extracts two distinct features from the signal spectrum i.e. the maximum pitch frequency and maximum cepstrum value. The 2D extracted feature set is passed on to the multi-level classification system constructed on supervised Support Vector Machine (SVM), which first discriminates between the person's gender and then classifies the person based on the gender. Total 120 samples were used to train the proposed classification system and the proposed system correctly identifies the speaker with the accuracy, specificity and sensitivity of 83.33% 86.67% and 80% respectively.