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This study investigates the discrimination between cold speech and healthy speech using features based on empirical mode decomposition (EMD). The EMD is employed to break down the signal into several intrinsic mode functions (IMFs). From each IMF, various statistical values like minimum, maximum, mean, standard deviation, first, second, and third quartiles, skewness, kurtosis, and energy of each IMF are extracted and used as a feature for distinguishing cold and healthy speech. The T-test examines the importance of EMD-based features for classifying cold speech. EMD-based feature performance is assessed using the deep neural network (DNN) classifier. The findings show that EMD-based features effectively discriminate between cold and healthy speech classes. Combining Mel-Frequency Cepstral Coefficients (MFCC) characteristics with EMD-based features improves the performance for identifying …