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<p>The integration of artificial intelligence into talent acquisition has accelerated the development of multimodal frameworks for employability assessment, offering greater accuracy, scalability, and objectivity. In this paper, we propose a novel Multimodal Deep Learning Framework that unifies textual resumes, video interviews, and audio responses into a single, interpretable decision system. Our approach leverages state-of-the-art neural architectures, including Transformer-based models for textual analysis, wav2vec 2.0 embeddings for speech, and three-dimensional facial expression modeling with EfficientNet and OpenFace. A dynamic attention-driven fusion module adaptively balances contributions from different modalities, while built-in explainability mechanisms support transparent and fair decision-making. We evaluate the framework on a rigorously curated multimodal dataset annotated by HR professionals. Results demonstrate that our method outperforms unimodal and hybrid baselines, achieving a 14% improvement in F1-score and 90% top-1 accuracy in employability prediction. Importantly, integrated bias mitigation techniques reduce gender- and ethnicity-related disparities by more than 25%, underscoring the framework’s potential for fair, responsible, and practical AI deployment in modern talent analytics.</p>

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Employability assessment using multimodal deep learning framework

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年3月13日

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  • Fengjin Zhou

摘要

The integration of artificial intelligence into talent acquisition has accelerated the development of multimodal frameworks for employability assessment, offering greater accuracy, scalability, and objectivity. In this paper, we propose a novel Multimodal Deep Learning Framework that unifies textual resumes, video interviews, and audio responses into a single, interpretable decision system. Our approach leverages state-of-the-art neural architectures, including Transformer-based models for textual analysis, wav2vec 2.0 embeddings for speech, and three-dimensional facial expression modeling with EfficientNet and OpenFace. A dynamic attention-driven fusion module adaptively balances contributions from different modalities, while built-in explainability mechanisms support transparent and fair decision-making. We evaluate the framework on a rigorously curated multimodal dataset annotated by HR professionals. Results demonstrate that our method outperforms unimodal and hybrid baselines, achieving a 14% improvement in F1-score and 90% top-1 accuracy in employability prediction. Importantly, integrated bias mitigation techniques reduce gender- and ethnicity-related disparities by more than 25%, underscoring the framework’s potential for fair, responsible, and practical AI deployment in modern talent analytics.