Supra-segmental information mainly consists of pitch, intonation, accent and duration. In character domain , they are fundamental frequency, it’s sequence information, energy sequence information and syllable length. they are all important features in speaker recognition system. After analyzing previous methods of extracting these characters, the method was proposed that speech is divided into single syllable firstly, then tone, energy, syllable length and other features were extracted next, thirdly Bigram model was generated as the Supra-Segmental Information model. The Bigram model was used as the character in an assistant speaker recognition system which assists the main speaker recognition system which uses MFCC parameters to make decisions. The experimental results show that the EER decreased 10.5% in text-independent speaker recognition system after joining the Supra-Segmental Information.