Forensic Gender Speaker Recognition under Clean and Noisy Environments

The Forensic Gender Speaker Recognition (FGSR) deals with finding out the gender of a person from his or her voice. This task has been implemented in several Automatic Speaker Recognition systems and has proved to be of great significance. The use of gender recognition eases for user authentication and identification in high security systems. In this paper, we have used the gender recognition for speakers using Mel Frequency Cepstral Coefficients (MFCCs), a Gaussian Mixture Model-Universal Background Model (GMM-UBM) and Likelihood Report (LR) were used for modelization of features extracted from the speech signal of speakers under clean and noisy conditions. The performance of FGSR system is evaluated in terms of Equal Proportion Probability (EPP). Three sets of experiments were performed - the first experiment; corpus contains males’ voices. The second; contains females’ voices and third; contains both voices of both sexes. In clean conditions, the average accuracy of the first experiment was slightly higher for EPP=3.1579% than the second and the third experiments. Therefore, Performance evaluation results are encouraging in clean conditions compared to in noisy condition for EPP=27%.