Analysis of Speaker's Age Using Clustering Approaches With Emotionally Dependent Speech Features

Abstract

Emotions are age, gender, culture, speaker, and situationally dependent. Due to an underdeveloped vocal tract or the vocal folds of children and a weak or aged speech production mechanism of older adults, the acoustic properties differ with the age of a person. In this sense, the features describing the age and emotionally relevant information of human voice also differ. This motivates the authors to investigate a number of issues related to database collection, feature extraction, and clustering algorithms for effective characterization and identification of human age of his or her paralanguage information. The prosodic features such as the speech rate, pitch, log energy, and spectral parameters have been explored to characterize the chosen emotional utterances whereas the efficient K-means and Fuzzy C-means clustering algorithms have been used to partition age-related emotional features for a better understanding of the related issues.