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The significance of features derived from complex analytic domain representation of speech, for different applications, is investigated. Frequency domain linear prediction (FDLP) coefficients are derived from analytic magnitude and instantaneous frequency (IF) coefficients are derived from analytic phase of speech signals. Minimal pair ABX (MP-ABX) tasks are used to analyse different features and develop insights into the nature of information in them. The performance of the features derived from analytic representation are compared with performance of the Mel-Frequency Cepstral Coefficients (MFCC). It is noticed that the magnitude based features- FDLP and MFCC delivered promising PaC, PaT and CaT scores in MP-ABX tasks, demonstrating their phoneme discrimination abilities. Combining FDLP features with MFCC had proven beneficial in phoneme discrimination tasks. The IF features performed well in TaP mode of MP-ABX tasks, emphasizing the existence of speaker specific information in them. The IF significantly outperformed FDLP, MFCC and their combination in speaker discrimination task.