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Speaker identification performance is almost perfect in neutral talkingenvironments; however, the performance is deteriorated significantly in shoutedtalking environments. This work is devoted to proposing, implementing andevaluating new models called Second-Order Circular Suprasegmental Hidden MarkovModels (CSPHMM2s) to alleviate the deteriorated performance in the shoutedtalking environments. These proposed models possess the characteristics of bothCircular Suprasegmental Hidden Markov Models (CSPHMMs) and Second-OrderSuprasegmental Hidden Markov Models (SPHMM2s). The results of this work showthat CSPHMM2s outperform each of: First-Order Left-to-Right SuprasegmentalHidden Markov Models (LTRSPHMM1s), Second-Order Left-to-Right SuprasegmentalHidden Markov Models (LTRSPHMM2s) and First-Order Circular SuprasegmentalHidden Markov Models (CSPHMM1s) in the shouted talking environments. In suchtalking environments and using our collected speech database, average speakeridentification performance based on LTRSPHMM1s, LTRSPHMM2s, CSPHMM1s andCSPHMM2s is 74.6%, 78.4%, 78.7% and 83.4%, respectively. Speaker identificationperformance obtained based on CSPHMM2s is close to that obtained based onsubjective assessment by human listeners.

Employing Second-Order Circular Suprasegmental Hidden Markov Models to Enhance Speaker Identification Performance in Shouted Talking Environments