COVID-19 Patient Detection from Telephone Quality Speech Data

Respiratory Distress Detection from Telephone Speech using Acoustic and Prosodic Features

With the widespread use of telemedicine services, automatic assessment of health conditions via telephone speech can significantly impact public health. This work summarizes our preliminary findings on automatic detection of respiratory distress using well-known acoustic and prosodic features. Speech samples are collected from de-identified telemedicine phonecalls from a healthcare provider in Bangladesh. The recordings include conversational speech samples of patients talking to doctors showing mild or severe respiratory distress or asthma symptoms. We hypothesize that respiratory distress ma…

面向低资源医疗领域会话电话语音的混合ASR系统开发

Development of Hybrid ASR Systems for Low Resource Medical Domain Conversational Telephone Speech

本文针对德语-阿拉伯语和德语-越南语医疗会话电话语音翻译任务，开发混合ASR系统，通过数据收集、多种声学模型架构设计及方言困难处理，支持非专业口译员以改善医患沟通。

迁移学习：在健康人咳嗽检测模型中增量添加患者咳嗽以检测COVID-19咳嗽

Transfer Learning to Detect COVID-19 Coughs with Incremental Addition of Patient Coughs to Healthy People's Cough Detection Models

本文提出增量迁移学习方法，利用健康人咳嗽模型和少量患者咳嗽数据，以合理准确度检测COVID-19咳嗽，减少对大型患者数据集的依赖，助力新型呼吸道病毒早期检测。

Audio feature ranking for sound-based COVID-19 patient detection

Audio classification using breath and cough samples has recently emerged as a low-cost, non-invasive, and accessible COVID-19 screening method. However, a comprehensive survey shows that no application has been approved for official use at the time of writing, due to the stringent reliability and accuracy requirements of the critical healthcare setting. To support the development of Machine Learning classification models, we performed an extensive comparative investigation and ranking of 15 audio features, including less well-known ones. The results were verified on two independent COVID-19 so…

QUCoughScope: An Artificially Intelligent Mobile Application to Detect Asymptomatic COVID-19 Patients using Cough and Breathing Sounds

In the break of COVID-19 pandemic, mass testing has become essential to reduce the spread of the virus. Several recent studies suggest that a significant number of COVID-19 patients display no physical symptoms whatsoever. Therefore, it is unlikely that these patients will undergo COVID-19 test, which increases their chances of unintentionally spreading the virus. Currently, the primary diagnostic tool to detect COVID-19 is RT-PCR test on collected respiratory specimens from the suspected case. This requires patients to travel to a laboratory facility to be tested, thereby potentially infectin…

Diagnosis of COVID-19 and Non-COVID-19 Patients by Classifying Only a Single Cough Sound

In this study, we proposed a machine learning-based system to distinguish patients with COVID-19 from non-COVID-19 patients by analyzing only a single cough sound. Two different data sets were used, one accessible for the public and the other available on request. After combining the data sets, the features were obtained from the cough sounds using the mel-frequency cepstral coefficients (MFCCs) method, and then they were classified with seven different machine learning classifiers. To determine the optimum values of hyperparameters for MFCCs and classifiers, the leave-one-out cross-validation…