Building a Speaker Recognition System with one Sample

Abstract

Speaker recognition system is the process of automatically recognizing the person from his/her speech. To correctly recognize a speaker by the system, many speech samples are needed at different times from each speaker. However, in some applications, such as forensic, the number of samples for each speaker is very limited. In this paper, a method is proposed to train the speaker recognition system based on only one speech sample. From that one sample, other samples are generated. The intent is to provide a complete speaker recognition system, without bothering the speaker to record the speech samples at different times. For this purpose, the speech samples are modified without altering the pitch and the speaker dependent features. Many techniques are used to generate new samples and apply these to the system, when the recognition system is based on the hidden Markov model. The system is built using the HTK software which is a hidden Markov model kit, and the best recognition rate is 85.86%.