Close Speakers Model and Comparative Study in Automatic Speaker Verification

The performance of speaker verification system degrades when the test segments are utterances of short duration, therefore, we investigate the use of model representing our target speaker with his close speaker and his own speech data. We propose to create a new Speaker Model who groups close speakers (CS) achieved with two clustering algorithms in Automatic Speaker Verification A.S.V. Intra and Inter speaker’s variability are two clustering algorithm used in voice module. We compare the traditional approach which uses one specific customer model (Maximum a Posteriori Adaptation) with the Close Speaker model (Customers Families).Close Speaker Model (CSM) applied only when speaker model is weak achieves 42% of equal error rate. The results demonstrate that the log likelihood of close speakers is greater than the likelihood of client speaker. The false alarm from client and CSM are closest and we are constrained to enhance speaker model.