MFF-Net: Multi-Feature Fusion Network for Speech Deception Detection

Speech deception detection often suffers from overly narrow feature representations and limited robustness under changing recording conditions. To address these limitations, we propose MFF-Net, a multi-feature fusion network with three modules. The first module uses a Gated-BiLSTM with attention to model sequences of acoustic-prosodic (ACO) and nonlinear dynamic (NLD) features. The second module uses a hybrid Transformer–CNN branch on Mel spectrograms, with a Levenberg–Marquardt (LM) refinement stage for the Mel-branch classifier. The third module fuses the two branches via cross-attention before a softmax decision. On the English Real-Life Trial Deception Database (RTDD), MFF-Net achieves 79.22% accuracy and 77.69% F1. On our Chinese Lie Dataset (CLD), it reaches 78.29% accuracy and 79.13% F1, outperforming strong baselines. Together with speaker-aware evaluation and clarified training objectives, the results support MFF-Net as a reproducible approach for deception detection across complementary corpora.