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The use of speech as a biomedical signal for diagnosing COVID‐19 is investigated using statistical analysis of speech spectral features and classification algorithms based on machine learning. It is established that spectral features of speech, obtained by computing the short‐time Fourier Transform (STFT), get altered in a statistical sense as a result of physiological changes. These spectral features are then used as input features to machine learning‐based classification algorithms to classify them as coming from a COVID‐19 positive individual or not. Speech samples from healthy as well as “asymptomatic” COVID‐19 positive individuals have been used in this study. It is shown that the RMS error of statistical distribution fitting is higher in the case of speech samples of COVID‐19 positive speech samples as compared to the speech samples of healthy individuals. Five state‐of‐the‐art machine learning classification …