MODELING OF PRE-TRAINED NEURAL NETWORK EMBEDDINGS LEARNED FROM RAW WAVEFORM FOR COVID-19 INFECTION DETECTION

COVID-19 is a respiratory system disorder that can disrupt the function of lungs. Effects of dysfunctional respiratory mechanism can reflect upon other modalities which function in close coupling. Audio signals result from modulation of respiration through speech production system, and hence acoustic information can be modeled for detection of COVID-19. In that direction, this paper is addressing the second DiCOVA challenge that deals with COVID-19 detection based on speech, cough and breathing. We investigate modeling of (a) ComParE LLD representations derived at frame- and turn-level resolutions and (b) neural representations obtained from pre-trained neural networks trained to recognize phones and estimate breathing patterns. On Track 1, the ComParE LLD representations yield a best performance of 78.05% area under the curve (AUC). Experimental studies on Track 2 and Track 3 demonstrate that neural representations tend to yield better detection than ComParE LLD representations. Late fusion of different utterance level representations of neural embeddings yielded a best performance of 80.64% AUC.