Multimodal Depression Classification Using Articulatory Coordination Features And Hierarchical Attention Based Text Embeddings

Autor:	Seneviratne, Nadee, Espy-Wilson, Carol
Rok vydání:	2022
Předmět:	Electrical Engineering and Systems Science - Audio and Speech Processing Computer Science - Computation and Language
Druh dokumentu:	Working Paper
Popis:	Multimodal depression classification has gained immense popularity over the recent years. We develop a multimodal depression classification system using articulatory coordination features extracted from vocal tract variables and text transcriptions obtained from an automatic speech recognition tool that yields improvements of area under the receiver operating characteristics curve compared to uni-modal classifiers (7.5% and 13.7% for audio and text respectively). We show that in the case of limited training data, a segment-level classifier can first be trained to then obtain a session-wise prediction without hindering the performance, using a multi-stage convolutional recurrent neural network. A text model is trained using a Hierarchical Attention Network (HAN). The multimodal system is developed by combining embeddings from the session-level audio model and the HAN text model Comment: Accepted to ICASSP 2022. arXiv admin note: text overlap with arXiv:2104.04195
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2202.06238 Zobrazit plný text záznamu View this record from Arxiv