Computer
Umniah Hameed Jaid; alia karim Abdulhassan
Abstract
The voice signal carries a wide range of data about the speaker, including theirphysical characteristics, feelings, and level of health. There are several uses for the estimateof these physical characteristics from the speech in forensics, security, surveillance,marketing, and customer service. ...
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The voice signal carries a wide range of data about the speaker, including theirphysical characteristics, feelings, and level of health. There are several uses for the estimateof these physical characteristics from the speech in forensics, security, surveillance,marketing, and customer service. The primary goal of this research is to identify the auditorycharacteristics that aid in estimating a speaker’s age. To this end, an ensemble featureselection model is proposed that selects the best features from a baseline acoustic featurevector 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 isperformed by, first calculating the feature importance or ranks based on individual featureselection 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 theTIMIT dataset and achieved a mean absolute error (MAE) of 5.58 years and 5.12 years formale and female age estimation
