Kexin Feng

A Knowledge-Driven Vowel-Based Approach of Depression Classification from Speech Using Data Augmentation

We propose a novel explainable machine learning (ML) model that identifies depression from speech, by modeling the temporal dependencies across utterances and utilizing the spectrotemporal information at the vowel level. Our method first models the variable-length utterances at the local-level into…