Classification of voices that elicit soothing effect by applying a voiced vs. unvoiced feature engineering strategy

This paper introduces a novel approach of classifying voices that elicit a soothing effect on listeners from a domain knowledge inspired application of feature engineering. In particular, we utilize the characteristics of voiced vs, unvoiced speech in order to build a more accurate feature set. Large sets of training data are prepared and disciplined feature selections are conducted. Our final classifier achieved 86.84% classification accuracy of cross validation and evaluations by unknown listener population via crowdsourcing have rates of agreement with the classification model range from 80% to 90%. The technologies are deployed into Jobaline products to help service companies identify hourly-job workers whose voice can elicit soothing effect on customers.