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SPEAKER RECOGNITION SYSTEM

Platform : DSP

IEEE Projects Years : 2005

This paper appears in: Automatic Speech Recognition and Understanding, 2005 IEEE Workshop on Issue Date: 27-27 Nov. 2005 AIM: To recognize the interested speaker among pool of speakers ABSTRACT This paper describes ICSI's 2005 speaker recognition system, which was one of the top performing systems in the NIST 2005 speaker recognition evaluation. The system is a combination of four sub-systems: 1) a keyword conditional HMM system, 2) an SVM-based lattice phone n-gram system, 3) a sequential nonparametric system, and 4) a traditional cepstral GMM System, developed by SRI. The first three systems are designed to take advantage of higher-level and long-term information. We observe that their performance is significantly improved when there is more training data. In this paper, we describe these sub-systems and present results for each system alone and in combination on the speaker recognition evaluation (SRE) 2005 development and evaluation data sets LEARNING OBJECTIVE: To understand the concept of speaker recognition system. INPUT: Pool of speech utterance of speakers OUTPUT : Detection of Speech utterance of interested speaker APPLICATIONS: Speech Processing SOFTWARE TOOL USED: MATLAB 2007A

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