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The human speech conveys much information such as semantic contents, emotion and even speaker identity. Our goal in this thesis is the task of text-independent speaker identification (SI) in large population applications. Identification (test) time has become one of the most important issues in recent real time systems. Identification time depends on the cost of likelihood computation between test features and registered speaker models. For real time application of SI, system must identify an unknown speaker quickly. Hence the conventional SI methods cannot be used. The main goal in this thesis is to propose several methods that reduced identification time without any loss of identification... 

The human speech signal conveys many levels of information ranging from phonetic content to speaker identity and even emotional status. This thesis deals with the task of open-set speaker identification (SI) from an unconstrained telephone conversation between two speakers. The goal is to find at most two speakers among a known set of target speakers that best match the voice samples of the input speech; the input voice samples are not constrained to the target speaker set. The uni-speaker problem is investigated first. The classic GMM-UBM system for text-independent SI and its adapted form are explored. The use of score-space information is advocated as a complementary source to the...