Evaluation of Prosodic and Voice Quality Features on Automatic Extraction of Paralinguistic Information

Abstract

Aiming to realize a non-verbal communication between humans and robots, the use of acoustic parameters related with voice quality features, besides classical prosodic features, is proposed and evaluated for automatic extraction of paralinguistic information (intentions, attitudes, and emotions) in dialog speech. Experimental results indicated that prosodic features were effective for detecting groups of paralinguistic information expressing specific functions (such as affirmation, denial, and asking for repetition), accounting for 61% of the global identification rate. Voice quality features were effective for detecting part of the paralinguistic information expressing emotions or attitudes (such as surprise, disgust and admiration), leading to 12 % improvement in the global identification rate