VoxCeleb1: Speaker Age-Group Classification using Probabilistic Neural Network
Abstract: The human voice speech includes essentially paralinguistic information used in many applications for voice recognition. Classifying speakers according to their age-group has been considered as a valuable tool in various applications, as issuing different levels of permission for different age-groups. In the presented research, an automatic system to classify speaker age-group without depending on the text is proposed. The Fundamental Frequency (F0), Jitter, Shimmer, and Spectral Sub-Band Centroids (SSCs) are used as a feature, while the Probabilistic Neural Network (PNN) is utilized as a classifier for the purpose of classifying the speaker utterances into eight age-groups. Experiments are carried out on VoxCeleb1 dataset to demonstrate the proposed system's performance, which is considered as the first effort of its kind. The suggested system has an overall accuracy of roughly 90.25%, and the findings reveal that it is clearly superior to a variety of base-classifiers in terms of overall accuracy.
Keywords: Speaker age-group recognition, features fusion, SSC, F0, jitter and shimmer.
Received May 23, 2020; accepted October 21, 2021
https://doi.org/10.34028/iajit/19/6/2