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Speaker recognition performance in emotional talking environments is not ashigh as it is in neutral talking environments. This work focuses on proposing,implementing, and evaluating a new approach to enhance the performance inemotional talking environments. The new proposed approach is based onidentifying the unknown speaker using both his/her gender and emotion cues.Both Hidden Markov Models (HMMs) and Suprasegmental Hidden Markov Models(SPHMMs) have been used as classifiers in this work. This approach has beentested on our collected emotional speech database which is composed of sixemotions. The results of this work show that speaker identification performancebased on using both gender and emotion cues is higher than that based on usinggender cues only, emotion cues only, and neither gender nor emotion cues by7.22%, 4.45%, and 19.56%, respectively. This work also shows that the optimumspeaker identification performance takes place when the classifiers arecompletely biased towards suprasegmental models and no impact of acousticmodels in the emotional talking environments. The achieved average speakeridentification performance based on the new proposed approach falls within2.35% of that obtained in subjective evaluation by human judges.

Employing both Gender and Emotion Cues to Enhance Speaker Identification Performance in Emotional Talking Environments