Multimodal Approach for Assessing Neuromotor Coordination in Schizophrenia Using Convolutional Neural Networks
This work addresses a domain-specific problem for clinical diagnosis of schizophrenia, offering an incremental improvement in detection accuracy.
The study tackled the problem of detecting schizophrenia with strong positive symptoms by analyzing speech articulatory coordination using video and audio data, achieving an 18% improvement in mean F1 score over a baseline method.
This study investigates the speech articulatory coordination in schizophrenia subjects exhibiting strong positive symptoms (e.g. hallucinations and delusions), using two distinct channel-delay correlation methods. We show that the schizophrenic subjects with strong positive symptoms and who are markedly ill pose complex articulatory coordination pattern in facial and speech gestures than what is observed in healthy subjects. This distinction in speech coordination pattern is used to train a multimodal convolutional neural network (CNN) which uses video and audio data during speech to distinguish schizophrenic patients with strong positive symptoms from healthy subjects. We also show that the vocal tract variables (TVs) which correspond to place of articulation and glottal source outperform the Mel-frequency Cepstral Coefficients (MFCCs) when fused with Facial Action Units (FAUs) in the proposed multimodal network. For the clinical dataset we collected, our best performing multimodal network improves the mean F1 score for detecting schizophrenia by around 18% with respect to the full vocal tract coordination (FVTC) baseline method implemented with fusing FAUs and MFCCs.