Our voices can convey many different types of thoughts and intent; how our voices carry them is often not consciously controlled and as a consequence, unintended effects may arise that negatively impact our relationships. How we say things is as important as what we say. This paper presents methodologies for computing a set of physical properties from sound waves of a speaker's voice directly, referred to as acoustic measures. Experiments are designed and conducted to establish the correlations between physical properties and auditory measures for human perception of sound waves. Based on these correlations, a voice coaching app can guide users, in real-time or deferred retrospective, to modify their speech's auditory measures, such as rate of speech, energy level, and intonation, to achieve their intended communication goals.

Web and mobile technologies enable ubiquitous access to information. Yet, it is getting harder, even for subject matter experts, to quickly identify quality, trustworthy, and reliable content available online through search engines powered by advanced knowledge graphs. This paper explores the practical applications of Domain Specific Knowledge Graphs that allow for the extraction of information from trusted published and unpublished sources, to map the extracted information to an ontology defined in collaboration with sector experts, and to enable the public to go from single queries into ongoing conversations meeting their knowledge needs reliably. We focused on Social-Impact Funding, an area of need for over one million nonprofit organizations, foundations, government entities, social entrepreneurs, impact investors, and academic institutions in the US.

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.