Abstract: The Applicants have developed a system and methods for extracting timing and emotional content from recorded audio in order to automate screening decisions for hiring candidates by processing candidate audio responses to predict candidate alignment for given job position. Emotional content is extracted using variable models to optimize detection of specific emotional content of interest. A feedback system is implemented for job supervisors to rate employee performance. Jobs are categorized according to emotional requirements and feedback is used to optimize candidate emotional alignment for a given position.

Abstract: The Applicant has developed a system and method for extracting a large amount of raw emotional features from candidate audio responses and automatically isolating the relevant features. Relative rankings for each pool of candidates applying for a given position are calculated and candidates are grouped by predictive scores into broad categories.

Abstract: The system and method of the present application utilizes a number of analysis modules to apply analysis techniques to candidate applications. The analysis modules then apply a score for each candidate for each technique with feedback information into an aggregate score. The system and method of the present application controls the collection order of the scores can weight scores by technique, and provide a graphical user interface for ease of evaluation.