Abstract-We consider the task of automatically predicting spirometry readings from cough and wheeze audio signals for asthma severity monitoring. Spirometry is a pulmonary function test used to measure forced expiratory volume in one second (FEV1) and forced vital capacity (FVC) when a subject exhales in the spirometry sensor after taking a deep breath. FEV1%, FVC% and their ratio are typically used to determine the asthma severity. Accurate prediction of these spirometry readings from cough and wheeze could help patients to non-invasively monitor their asthma severity in the absence of spirometry. We use statistical spectrum description (SSD) as the cue from cough and wheeze signal to predict the spirometry readings using support vector regression (SVR). We perform experiments with cough and wheeze recordings from 16 healthy persons and 12 patients. We find that the coughs are better predictor of spirometry readings compared to the wheeze signal. FEV1%, FVC% and their ratio are predicted with root mean squared error of 11.06%, 10.3% and 0.08 respectively. We also perform a three class asthma severity level classification with predicted FEV1% and obtain an accuracy of 77.77%.
For the benefit of spoken language training, concatenation based articulatory video synthesis has been proposed in the past to overcome the limitation in the articulatory data recording. For this, real time magnetic resonance imaging (rt-MRI) video image-frames (IFs) containing articulatory movements have been used. These IFs require a visual augmentation for better understanding. We, in this work, propose an augmentation method using pixel intensities in the regions enclosed by the articulatory boundaries obtained from air-tissue boundaries (ATBs). Since, the pixel intensities reflect the muscle movements in the articulators, the augmented IFs could provide realistic articulatory movements, when we color them accordingly. However, the ATB manual annotation is time consuming; hence, we propose to synthesize ATBs using the ATBs from a few selected frames that have been used in synthesizing the articulatory videos. We augment a set of synthesized articulatory videos for 50 words obtained from the MRI-TIMIT database. Subjective evaluation on the quality of the augmented videos using twenty-one subjects suggests that the videos are visually more appealing than the respective synthesized rt-MRI videos with a rating of 3.75 out of 5, where a score of 5 (1) indicates that the augmented video quality is excellent (poor).
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