Abstract— Parkinson’s disease (PD) is a progressive neurological disorder prevalent in old age. Past researches have shown that speech can be used as an early marker for identification of PD. It affects a number of speech components such as phonation, speech intensity, articulation, and respiration, which alters the speech intelligibility. Speech feature extraction and classification always have been challenging tasks due to the existence of non-stationary and discontinuity in the speech signal. In this study, Empirical mode decomposition (EMD) based features are demonstrated to capture the mentioned characteristics. A new feature, intrinsic mode function cepstral coefficient (IMFCC) is proposed to efficiently represent the characteristics of Parkinson speech. The performances of proposed features are assessed with two different datasets: dataset1 and dataset 2 each having 20 normal and 25 Parkinson affected peoples. From the results, it is demonstrated that the proposed intrinsic mode function cepstral coefficient feature provides the superior classification accuracy of both datasets. There is a significant increase of 10-20 % in accuracy compared to the standard acoustic and Mel frequency cepstral coefficient (MFCC) features.