A Novel Arabic Text-independent Speaker Verification System based on Fuzzy Hidden Markov Model

The most important shortcoming of the current speaker verification methods (based on knowledge or possession) is that this process is not sure whether the holder of the given ID is the entitled one or an imposter. Using biometrics in the verification system is designated for minimizing this problem and decreasing the necessity of carrying the tokens. In this paper, a novel Arabic text-independent speaker verification system is presented. First of all, new speech features are proposed for speaker characterization, which denoted as Wavelet Packet Four-Directional Features (WPFDF). With the objective of speaker verification, the paper proposes a Fuzzy Hidden Markov Model, termed FHMM, where the kernel fuzzy c-means (KFCM) is extended to calculate fuzzy memberships of HMMs training samples. Thus, information loss is reduced as well as recognition rate is increased. The proposed approach reached 98.38% of recognition rate.