Maggie Mi
In the recent decade, scholars have been exploring the use of speech to aid diagnosis of illnesses (Brabenec, Mekyska, Galaz, & Rektorova, 2017; Brown et al., 2020, p.1). As coronavirus is a respiratory disease with symptoms that prompt pulmonic paralinguistic sounds, such as coughing, wheezing, and voicing, it is predicted that these features will have acoustic significances and patterns that can be used to train two Machine Learning models, namely, a Convolutional Neural Network (CNN) and a Multilayer Perceptron (MLP) for COVID-19 diagnostics. Importantly, the majority of previous works overlooks the (socio-)phonetic/linguistic foundations for analysis (Banerjee et al., 2019; Brown et al., 2020). Therefore, what makes this research truly original, is the sociophonetic experiments that will be tested on the system. These include, exploring which vowel carries the most ‘COVID-19 information’, the allophonic composition contribution to classification and whether anatomical differences due to gender, i.e., males having longer vocal tract (VT) length than females, would lead to significant differences in results. Rather than contributing the shortcomings of the system to algorithmic solution designs, it is just as important to consider VT anatomy and articulatory phonetics, to better understand what the data can reveal about speech production in COVID-19 patients.
Banerjee, D., Islam, K., Xue, K., Mei, G., Xiao, L., Zhang, G., . . . Li, J. (2019). A deep transfer learning approach for improved post-traumatic stress disorder diagnosis. Knowledge and Information Systems, 60(3), 1693-1724.
Brabenec, L., Mekyska, J., Galaz, Z., & Rektorova, I. (2017). Speech disorders in Parkinson’s disease: early diagnostics and effects of medication and brain stimulation. Journal of neural transmission, 124(3), 303-334.
Brown, C., Chauhan, J., Grammenos, A., Han, J., Hasthanasombat, A., Spathis, D., . . . Mascolo, C. (2020). Exploring automatic diagnosis of COVID-19 from crowdsourced respiratory sound data. arXiv preprint arXiv:2006.05919.