Detection of COVID-19 from speech signal using bio-inspired based cepstral features

•Extraction and analysis of the cepstral features used in speech recognition. The optimization of the conversion scale in the frequency domain, and the frequency range of filter bank using the bio-inspired technique to facilitate COVID-19 detection.•Identification of the best possible sound patterns during coughing, breathing, and voiced sounds to efficiently detect COVID-19.•Application of the speech enhancement schemes for the improvement in the classification performance.•Use of the Adaptive Synthetic Sampling Approach for Imbalanced Learning to remove the class imbalance in the database and to evaluate properly the various performance measures of the proposed classifier.•Using the same classifier, comparison of the detection performance of the proposed cepstral features as inputs with different existing cepstral features employing two separate standard databases.•Demonstration of overall 5% enhancement in detection performance compared to that of other four existing features based method. The early detection of COVID-19 is a challenging task due to its deadly spreading nature and existing fear in minds of people. Speech-based detection can be one of the safest tools for this purpose as the voice of the suspected can be easily recorded. The Mel Frequency Cepstral Coefficient (MFCC) analysis of speech signal is one of the oldest but potential analysis tools. 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. Traditionally, in speech recognition, these values are fixed. But the characteristics of speech signals vary from disease to disease. 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. By exploiting these properties the efficiency of the COVID-19 detection can be improved. To achieve this objective the frequency range and the conversion scale of frequencies have been suitably optimized. Further to enhance the accuracy of detection performance, speech enhancement has been carried out before extraction of features. By implementing these two concepts a new feature called COVID-19 Coefficient (C-19CC) is developed in this paper. Finally, the performance of these features has been compared.