How Does a Face Mask Impact Speech Perception?

Face masks are mandatory during the COVID-19 pandemic, leading to attenuation of sound energy and loss of visual cues which are important for communication. This study explores how a face mask affects speech performance for individuals with and without hearing loss. Four video recordings (a female speaker with and without a face mask and a male speaker with and without a face mask) were used to examine individuals' speech performance. The participants completed a listen-and-repeat task while watching four types of video recordings. Acoustic characteristics of speech signals based on mask type (no mask, surgical, and N95) were also examined. The availability of visual cues was beneficial for speech understanding-both groups showed significant improvements in speech perception when they were able to see the speaker without the mask. However, when the speakers were wearing the mask, no statistical significance was observed between no visual cues and visual cues conditions. Findings of the study demonstrate that provision of visual cues is beneficial for speech perception for individuals with normal hearing and hearing impairment. This study adds value to the importance of the use of communication strategies during the pandemic where visual information is lost due to the face mask.

Evaluation of deep learning for COVID-19 diagnosis: Impact of image dataset organization.

INTRODUCTION: Coronavirus disease 2019 (COVID-19) has spread all over the world showing high transmissibility. Many studies have proposed diverse diagnostic methods based on deep learning using chest X-ray images focusing on performance improvement. In reviewing them, this study noticed that evaluation results might be influenced by dataset organization. Therefore, this study identified whether the high-performance values can prove the clinical application potential. METHODS: This study selected chest X-ray image databases which have been widely applied in previous studies. One database includes images for COVID-19, while the others consist of normal and pneumonia images. Then, the COVID-19 classification model was designed and trained on diverse database compositions and evaluated using confusion matrix-based metrics. Also, each database was analyzed by graphical representation methods. RESULTS: The performance was significantly different according to dataset composition. Overall, higher performance was identified on the dataset organized with different databases for each class, compared with the dataset from same database. Also, there were significant differences in the image characteristics between different databases. CONCLUSIONS: The experimental results indicate that model may be trained based on differences of the image characteristics between databases and not on lesion features. This shows that evaluation metrics can be influenced by dataset organization, and high metric values would not directly mean the potential for clinical application. These emphasize the importance of suitable dataset organization for applying COVID-19 diagnosis methods to real clinical sites. Radiologists should sufficiently understand about this issue as actual user of these methods.