Contribution of prosodic and cepstral features in improvment of a synthesized arabic speaker recognition task performance

An emerging need for biometric Speaker Verification (SV) and Identification (SI) systems is necessary for wireless remote access security in goal to be less vulnerable against distortion due to speech coding. This paper presents results on recognition system performed on the decoded speech of the G.729 codec. To show the performance loss due to distortion in the decoding process step, we are oriented to exploit the information contained within the source and the vocal tract resources. For this, SVM-based text-independent speaker classification was designed to use the information that combines the Mel Frequency Cepstral Coefficients (MFCC) features, the Energy, and the Pitch frequency. Experiments were performed over the Arabic spoken digits, the ARADIGIT database. The obtained results show that the best performance of Speaker recognition using G.729 decoded database is obtained by the combination of the prosodic features with an EER equal to 4,22%.