Two acoustic feature sets from the computational paralinguistics challenge and extended Geneva minimalistic acoustic parameter sets have been used as inputs to SVM to achieve an average classification accuracy of 69%.ġ.1.2. Audio dataset has been collected from twenty females and thirty-two males COVID19 patients from two hospitals in Wuhan, China during March 20, – 26, 2020. In another paper, the health condition of COVID-19 patients is categorized into four types with respect to the severity of illness, sleep quality, fatigue, and anxiety. A sensitivity of 91.2% for breathing based detection and a mean absolute error of 1.01 breaths per minute have been reported using the proposed methods. The log Mel spectra of a speech signal are mapped with the respiratory sensors to train the neural network-based models. It explains multi-modal approach using audio, text, and image for achieving better detection results. Ī review of Artificial Intelligence based methods used for COVID-19 detection is presented in. It is reported that a maximum accuracy of 80% in Receiver Operator Characteristics Area Under Curve (AUC). Several audio features such as speech time duration, onset, tempo, period, RMS energy, spectral centroid, Roll-Off frequency, Zero-crossing, MFCC have been used as inputs to classification methods such as Logistic Regression, Gradient Boosting Trees, and Support Vector Machines (SVM) for the classification task. A crowdsourced data set of respiratory sounds has been prepared using coughs and breathing sounds for detecting COVID-19. The details in terms of databases, feature extraction methods, and performance analysis have been presented. In the recent past, attempts have been made in the area of speech based COVID-19 analysis and diagnosis. Speech analysis is one of the important methods used for the detection of parkinson alzheimer, asthma. Review on COVID-19 detection using speech signals In this section, a review of related literature is carried out in two parts: speech based COVID-19 detection and speech recognition using MFCC features.ġ.1.1. It is a fact that the speech-based detection of COVID-19 is a simpler and safer approach for this purpose. Under such circumstances, it has become a huge challenge for developing an appropriate method for the early detection of this disease. It is observed from the experiences of the medical practitioners that rather than the deadly nature of the virus, its fear of stigma is stopping people from going to medical laboratories for testing purposes. The social distancing of 1.6 m to 3 m is recommended to control the rapid spreading of COVID-19 cases. As per the report of the World Health Organization, more than a hundred million people have suffered till 7th March 2021 out of which more than 2.5 million deaths have been reported. ![]() As reported, it has started in Wuhan, China in 2019 and has affected the whole world. ![]() Finally, the performance of these features has been compared.Ĭoronavirus disease 19 (COVID- 19) which exhibits acute respiratory syndrome is a deadly viral infection. ![]() By implementing these two concepts a new feature called COVID-19 Coefficient (C-19CC) is developed in this paper. Further to enhance the accuracy of detection performance, speech enhancement has been carried out before extraction of features. To achieve this objective the frequency range and the conversion scale of frequencies have been suitably optimized. By exploiting these properties the efficiency of the COVID-19 detection can be improved. In the case of detection of COVID-19, mainly the coughing sounds are used whose bandwidth and properties are quite different from the complete speech signal. But the characteristics of speech signals vary from disease to disease. Traditionally, in speech recognition, these values are fixed. The performance of this analysis mainly depends on the use of conversion between normal frequency scale to perceptual frequency scale and the frequency range of the filters used. The Mel Frequency Cepstral Coefficient (MFCC) analysis of speech signal is one of the oldest but potential analysis tools. Speech-based detection can be one of the safest tools for this purpose as the voice of the suspected can be easily recorded. The early detection of COVID-19 is a challenging task due to its deadly spreading nature and existing fear in minds of people.
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