Paralinguistic profiling using speech recognition

This research explores the various indicators for non-verbal cues of speech and provides a method of building a paralinguistic profile of these speech characteristics which determines the emotional state of the speaker. Since a major part of human communication consists of vocalization, a robust approach that is capable of classifying and segmenting an audio stream into silent and voiced regions and developing a paralinguistic profile for the same is presented. The data consisting of disruptions is first segmented into frames and this data is analyzed by exploiting short term acoustic features, temporal characteristics of speech and measures of verbal productivity. A matrix is finally developed relating the paralinguistic properties of average pitch, energy, rate of speech, silence duration and loudness to their respective context. Happy and confident states possessed high values of energy and rate of speech and less silence duration whereas tense and sad states showed low values of energy and speech rate and high periods of silence. Paralanguage was found to be an important cue to decipher the implicit meaning in a speech sample.