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In this paper, Suprasegmental Hidden Markov Models (SPHMMs) have been used toenhance the recognition performance of text-dependent speaker identification inthe shouted environment. Our speech database consists of two databases: ourcollected database and the Speech Under Simulated and Actual Stress (SUSAS)database. Our results show that SPHMMs significantly enhance speakeridentification performance compared to Second-Order Circular Hidden MarkovModels (CHMM2s) in the shouted environment. Using our collected database,speaker identification performance in this environment is 68% and 75% based onCHMM2s and SPHMMs respectively. Using the SUSAS database, speakeridentification performance in the same environment is 71% and 79% based onCHMM2s and SPHMMs respectively.

Speaker Identification in the Shouted Environment Using Suprasegmental Hidden Markov Models