A Speaker Recognition Method Based on Personal Identification Voice and Trapezoidal Fuzzy Similarity

A text-dependent speaker recognition method is proposed using trapezoidal fuzzy similarity function to measure the similarity of voice features between a test user and the registered speaker who has nearest distance. The trapezoidal fuzzy similarity function is constructed based on three-time data recorded during enrolment process as personal identification voice (PIV) and statistical data of an individual recorded many times in a long time period to cover the intra-variation. A set of acoustic voice features is also introduced to present some general speaker and text dependent characteristics that are effective for modeling PIV, thus allowing to capture the inter-variation from one speaker to another. The experimental results on 24 speakers recorded in four different sessions show that, without false acceptation, the proposed system can decrease 30.05% of false rejection cases, compared to the traditional nearest neighbor approach. The focus of this work is on applications which require fast processing and few burdens for users.