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

Speaker identification performance is almost perfect in neutral talking environments. However, the performance is deteriorated significantly in shouted talking environments. This work is devoted to proposing, implementing, and evaluating new models called Second-Order Circular Suprasegmental Hidden Markov Models (CSPHMM2s) to alleviate the deteriorated performance in the shouted talking environments. These proposed models possess the characteristics of both Circular Suprasegmental Hidden Markov Models (CSPHMMs) and Second-Order Suprasegmental Hidden Markov Models (SPHMM2s). The results of this work show that CSPHMM2s outperform each of First-Order Left-to-Right Suprasegmental Hidden Markov Models (LTRSPHMM1s), Second-Order Left-to-Right Suprasegmental Hidden Markov Models (LTRSPHMM2s), and First-Order Circular Suprasegmental Hidden Markov Models (CSPHMM1s) in the shouted talking environments. In such talking environments and using our collected speech database, average speaker identification performance based on LTRSPHMM1s, LTRSPHMM2s, CSPHMM1s, and CSPHMM2s is 74.6%, 78.4%, 78.7%, and 83.4%, respectively. Speaker identification performance obtained based on CSPHMM2s is close to that obtained based on subjective assessment by human listeners.