Title: Speaking Style Authentication Using Suprasegmental Hidden Markov Models,
Abstract: The importance of speaking style authentication from human speech is gaining
an increasing attention and concern from the engineering community. The
importance comes from the demand to enhance both the naturalness and efficiency
of spoken language human-machine interface. Our work in this research focuses
on proposing, implementing, and testing speaker-dependent and text-dependent
speaking style authentication (verification) systems that accept or reject the
identity claim of a speaking style based on suprasegmental hidden Markov models
(SPHMMs). Based on using SPHMMs, our results show that the average speaking
style authentication performance is: 99%, 37%, 85%, 60%, 61%, 59%, 41%, 61%,
and 57% belonging respectively to the speaking styles: neutral, shouted, slow,
loud, soft, fast, angry, happy, and fearful.