Hilbert-Huang Transform-Based Time-Frequency Analysis of Speech Signals for the Identification of Common Cold

The current advancements in machine learning research pertaining to speech and health are highly interesting. One aspect of speech-processing research that is gaining popularity is the use of computational paralinguistic analysis to evaluate a variety of health conditions. In this study, we have used the Hilbert-Huang transform (HHT) for the time-frequency analysis of speech signals for the identification of the common cold. The HHT is a time-frequency transform that is adaptive and ideal for non-linear and non-stationary signals. The HHT is a combination of empirical mode decomposition (EMD) and the Hilbert transform (HT). The HHT gives the time-frequency representation (TFR) matrix of the speech signal. Then, the entropy of each frequency component in TFR is computed and used as a distinguishing feature between cold and healthy speech. The efficacy of the proposed methodology is evaluated on the URTIC dataset using a deep neural network. The proposed features achieve UARs of 65.66% and 65.26%, respectively, on the develop and test partitions. The results of the study demonstrate that the time-frequency entropy features extracted using the HHT are effective in distinguishing between cold and healthy speech.