Speaking Style Authentication Using Suprasegmental Hidden Markov Models
This work addresses the need for more natural and efficient spoken language interfaces, though it appears incremental as it applies an existing method to a specific domain.
The researchers tackled the problem of authenticating speaking styles from speech to improve human-machine interfaces, achieving performance ranging from 37% to 99% across nine different styles using suprasegmental hidden Markov models.
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.