Document Type : Research Paper
Authors
1 Computer Science Department, College of Science, University of Baghdad, Baghdad, Iraq
2 Computer Science Department, University of Technology, Baghdad, Iraq
Abstract
The voice signal carries a wide range of data about the speaker, including their
physical characteristics, feelings, and level of health. There are several uses for the estimate
of these physical characteristics from the speech in forensics, security, surveillance,
marketing, and customer service. The primary goal of this research is to identify the auditory
characteristics that aid in estimating a speaker’s age. To this end, an ensemble feature
selection model is proposed that selects the best features from a baseline acoustic feature
vector for age estimation from speech. Using a feature vector that covers various spectral,
temporal, and prosodic aspects of speech, an ensemble-based automatic feature selection is
performed by, first calculating the feature importance or ranks based on individual feature
selection methods, then voting is applied to the resulting feature ranks to attain the topranked subset by all feature selection methods. The proposed method is evaluated on the
TIMIT dataset and achieved a mean absolute error (MAE) of 5.58 years and 5.12 years for
male and female age estimation
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