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

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