Leveraging Multimodal Behavioral Analytics for Automated Job Interview Performance Assessment and Feedback (2006.07909v2)

Abstract: Behavioral cues play a significant part in human communication and cognitive perception. In most professional domains, employee recruitment policies are framed such that both professional skills and personality traits are adequately assessed. Hiring interviews are structured to evaluate expansively a potential employee's suitability for the position - their professional qualifications, interpersonal skills, ability to perform in critical and stressful situations, in the presence of time and resource constraints, etc. Therefore, candidates need to be aware of their positive and negative attributes and be mindful of behavioral cues that might have adverse effects on their success. We propose a multimodal analytical framework that analyzes the candidate in an interview scenario and provides feedback for predefined labels such as engagement, speaking rate, eye contact, etc. We perform a comprehensive analysis that includes the interviewee's facial expressions, speech, and prosodic information, using the video, audio, and text transcripts obtained from the recorded interview. We use these multimodal data sources to construct a composite representation, which is used for training machine learning classifiers to predict the class labels. Such analysis is then used to provide constructive feedback to the interviewee for their behavioral cues and body language. Experimental validation showed that the proposed methodology achieved promising results.

• The paper presents a multimodal framework that combines audio, video, and text data for automated job interview assessment.
• It demonstrates that integrating prosodic, lexical, and facial features significantly enhances performance prediction using classifiers like Random Forest.
• The approach provides actionable feedback to improve candidate performance and refine recruitment strategies.

Leveraging Multimodal Behavioral Analytics for Automated Job Interview Performance Assessment and Feedback

Introduction

The paper presents a framework that utilizes multimodal behavioral analytics to evaluate candidates' performance in job interviews. The system integrates facial expressions, speech, and prosodic information to derive a composite representation, which facilitates the feedback process regarding engagement, speaking rate, eye contact, and other behavioral metrics. The approach emphasizes the importance of both verbal and non-verbal cues in understanding and predicting candidates' suitability for roles, thereby aiding both recruiters and candidates in improving the recruitment process.

Related Work

A significant volume of research demonstrates the efficacy of multimodal data for sentiment and behavior analysis. Various previous studies have utilized visual and vocal data to assess emotional states and interpersonal communication cues. For instance, sentiment analysis using high-level visual features alongside linguistic cues has shown improved performance in emotion detection tasks. These foundations underscore the potential of combining diverse data modalities to enhance the understanding of candidate behavior in interview settings.

Proposed Approach

The proposed model comprises three primary modalities: audio, video, and text. Audio processing involves extracting prosodic features, leveraging time-domain, frequency-domain, and cepstral-domain characteristics to capture variations in pitch, intensity, and other relevant acoustic properties. For video data, facial landmarks and head poses are analyzed, with further classification of smiling using convolutional neural networks. Textual data is processed to derive lexical features such as speaking rates and vocabulary richness, supplementing the quantitative analysis with sentiment evaluations.

These multimodal features are unified into a comprehensive feature vector, fed into machine learning classifiers like Random Forest, Support Vector Machines, Multitask Lasso, and Multilayer Perceptrons to predict interview performance across various criteria.

Implementation and Feature Engineering

Multiple classifiers were evaluated for their ability to predict nine predefined performance labels. Various feature selection methods were applied to optimize the feature set before classification. Experiments with the MIT interview dataset demonstrated that the Random Forest classifier generally outperformed other models, especially when employed with comprehensive multimodal features, indicating its robustness in dealing with diverse input modalities.

The fusion of modalities and feature extraction techniques such as the usage of the Benjamini-Hochberg procedure for controlling false positives in feature selection was critical in achieving reliable and significant performance improvements.

Results and Analysis

Experimental results confirmed that multimodal analysis yields superior assessment capabilities compared to unimodal approaches. The system achieved its highest accuracy with the Reading Rate label, evidencing that integrating prosodic, lexical, and facial features provides a more nuanced understanding of candidate behavior. The Random Forest classifier consistently delivered high performance, particularly when aligned with sophisticated feature selection mechanisms.

Conclusion

The research underscores the effectiveness of a multimodal analytical framework in the automated assessment of job interview performance. This methodology could significantly enhance current practices by providing actionable feedback for candidates, thereby aiding them in self-improvement and interview preparation. Future work could expand the dataset to increase model robustness and explore additional features, such as para-verbal cues, to refine behavioral analysis further. Integrating such a system within web applications may facilitate broader deployment, assisting a larger pool of candidates in their job search endeavors.