Open Set Classification Based on Tolerance Interval for Speaker Verification System

• Main Authors: Hao Chung Yu, 俞皓中
• Other Authors: Hsu, Chun-Nan
• Format: Others
• Language: en_US
• Published: 2002
• Online Access: http://ndltd.ncl.edu.tw/handle/48804952821192817644

碩士 === 國立臺灣大學 === 資訊工程學研究所 === 90 === Speaker verification systems solve the problem of verifying whether a given utterance comes from a claimed speaker. This problem is important because an accurate speaker verification system can be applied to many security systems. Comparing to other biometric methods like fingerprint or face recognition, speaker verification systems do not require expensive specialized equipments and are effective especially for remote identity verification. Previously, Renoylds et al. have proposed a speaker verification system using Gaussian mixture model, but their system is incomplete because their system needs a set of background speaker models, which are constructed using a large speech database of a variety of speakers. It may not be feasible to obtain such a database in the real world. In this thesis, I propose a new solution called OSCILLO, for speaker verification. By applying tolerance interval technique in statistics, OSCILLO can verify a speaker's ID without background speaker models. This greatly reduces the size of the whole system and the time for both training and testing. We compare OSCILLO and Reynolds' method using three standard speech databases: TCC-300, TIMIT and NIST. The experimental results show that OSCILLO performs well for all databases.