Abstract
This study introduces LegalEye, a multimodal machine-learning model developed to detect deception in courtroom settings across three languages: English, Spanish, and Tagalog. The research investigates whether integrating audio, visual, and textual data can enhance deception detection accuracy and reduce bias in diverse legal contexts. LegalEye uses neural networks and late fusion techniques to analyze multimodal courtroom testimony data. The dataset was carefully constructed with balanced representation across racial groups (White, Black, Hispanic, Asian) and genders, with attention to minimizing implicit bias. Performance was evaluated using accuracy and AUC across individual and combined modalities. The model achieved high deception detection rates-97% for English, 85% for Spanish, and 86% for Tagalog. Late fusion of modalities outperformed single-modality models, with visual features being most influential for English and Tagalog, while Spanish showed stronger audio and textual performance. The Tagalog audio model underperformed due to frequent code-switching. Dataset balancing helped mitigate demographic bias, though Asian representation remained limited. LegalEye shows strong potential for language-adaptive and culturally sensitive deception detection, offering a robust tool for pre-trial interviews and legal analysis. While not suited for real-time courtroom decisions, its objective insights can support legal counsel and promote fairer judicial outcomes. Future work should expand linguistic and demographic coverage.