Title: Employing both Gender and Emotion Cues to Enhance Speaker Identification Performance in Emotional Talking Environments,
Abstract: Speaker recognition performance in emotional talking environments is not as
high as it is in neutral talking environments. This work focuses on proposing,
implementing, and evaluating a new approach to enhance the performance in
emotional talking environments. The new proposed approach is based on
identifying 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 been
tested on our collected emotional speech database which is composed of six
emotions. The results of this work show that speaker identification performance
based on using both gender and emotion cues is higher than that based on using
gender cues only, emotion cues only, and neither gender nor emotion cues by
7.22%, 4.45%, and 19.56%, respectively. This work also shows that the optimum
speaker identification performance takes place when the classifiers are
completely biased towards suprasegmental models and no impact of acoustic
models in the emotional talking environments. The achieved average speaker
identification performance based on the new proposed approach falls within
2.35% of that obtained in subjective evaluation by human judges.