Abstract: In an illustrative embodiment, systems and methods for automating recorded candidate assessments include receiving a submission for an available position including a question response recording for each of one or more interview questions. For each question response recording, a transcript can be generated by applying a speech-to-text algorithm to an audio portion of the recording. The systems and methods can detect, within the transcript, identifiers each associated with the personality aspects by applying a natural language classifier trained to detect words and phrases associated with the personality aspects of the personality model. Scores may be calculated for each of the personality aspects based on a relevance of the respective personality aspect to the respective interview question and detected identifiers. The scores can be presented within a user interface screen responsive to receiving a request to view interview results.

Abstract: In an illustrative embodiment, systems and methods for automating candidate video assessments include receiving a submission from a candidate for an available position including baseline response video segments and question response video segments. The system can determine, from detected nonverbal features within the baseline response video segments, nonverbal baseline scores. For each of the interview questions, candidate response attributes can be detected including a response direction, a response speed, and nonverbal features. A nonverbal reaction score is calculated from the detected nonverbal features and the baseline scores. A response score can be calculated from the response direction and response speed, and a trustworthiness score is determined based on a correspondence between the response score and the nonverbal reaction score. A next interview question can be determined in real-time from a benchmarked version of the response score.

Abstract: In an illustrative embodiment, systems and methods for automating recorded candidate assessments include receiving a submission for an available position including a question response recording for each of one or more interview questions. For each question response recording, a transcript can be generated by applying a speech-to-text algorithm to an audio portion of the recording. The systems and methods can detect, within the transcript, identifiers each associated with the personality aspects by applying a natural language classifier trained to detect words and phrases associated with the personality aspects of the personality model. Scores may be calculated for each of the personality aspects based on a relevance of the respective personality aspect to the respective interview question and detected identifiers. The scores can be presented within a user interface screen responsive to receiving a request to view interview results.

Abstract: In an illustrative embodiment, systems and methods for automating candidate video assessments include receiving a submission from a candidate for an available position including baseline response video segments and question response video segments. The system can determine, from detected nonverbal features within the baseline response video segments, nonverbal baseline scores. For each of the interview questions, candidate response attributes can be detected including a response direction, a response speed, and nonverbal features. A nonverbal reaction score is calculated from the detected nonverbal features and the baseline scores. A response score can be calculated from the response direction and response speed, and a trustworthiness score is determined based on a correspondence between the response score and the nonverbal reaction score. A next interview question can be determined in real-time from a benchmarked version of the response score.

Abstract: In an illustrative embodiment, systems and methods for automating recorded candidate assessments include receiving a submission for an available position including a question response recording for each of one or more interview questions. For each question response recording, a transcript can be generated by applying a speech-to-text algorithm to an audio portion of the recording. The systems and methods can detect, within the transcript, identifiers each associated with the personality aspects by applying a natural language classifier trained to detect words and phrases associated with the personality aspects of the personality model. Scores may be calculated for each of the personality aspects based on a relevance of the respective personality aspect to the respective interview question and detected identifiers. The scores can be presented within a user interface screen responsive to receiving a request to view interview results.

Abstract: In an illustrative embodiment, systems and methods for automating candidate video assessments include receiving a submission from a candidate for an available position including baseline response video segments and question response video segments. The system can determine, from detected nonverbal features within the baseline response video segments, nonverbal baseline scores. For each of the interview questions, candidate response attributes can be detected including a response direction, a response speed, and nonverbal features. A nonverbal reaction score is calculated from the detected nonverbal features and the baseline scores. A response score can be calculated from the response direction and response speed, and a trustworthiness score is determined based on a correspondence between the response score and the nonverbal reaction score. A next interview question can be determined in real-time from a benchmarked version of the response score.

Abstract: In an illustrative embodiment, systems and methods for automating recorded candidate assessments include receiving a submission for an available position including a question response recording for each of one or more interview questions. For each question response recording, a transcript can be generated by applying a speech-to-text algorithm to an audio portion of the recording. The systems and methods can detect, within the transcript, identifiers each associated with the personality aspects by applying a natural language classifier trained to detect words and phrases associated with the personality aspects of the personality model. Scores may be calculated for each of the personality aspects based on a relevance of the respective personality aspect to the respective interview question and detected identifiers. The scores can be presented within a user interface screen responsive to receiving a request to view interview results.