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.

We present the research, and product development and deployment, of Voice Analyzer' by Jobaline Inc. This is a patent pending technology that analyzes voice data and predicts human emotions elicited by the paralinguistic elements of a voice. Human voice characteristics, such as tone, complement the verbal communication. In several contexts of communication, "how" things are said is just as important as "what" is being said. This paper provides an overview of our deployed system, the raw data, the data processing steps, and the prediction algorithms we experimented with. A case study is included where, given a voice clip, our model predicts the degree in which a listener will find the voice "engaging". Our prediction results were verified through independent market research with 75% in agreement on how an average listener would feel. One application of Jobaline Voice Analyzer technology is for assisting companies to hire workers in the service industry where customers' emotional response to workers' voice may affect the service outcome. Jobaline Voice Analyzer is deployed in production as a product offer to our clients to help them identify workers who will better engage with their customers. We will also share some discoveries and lessons learned.