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In this paper, we present a spectro-temporal feature extraction technique using sub-band Hilbert envelopes of relatively long segments of speech signal. Hilbert envelopes of the sub-bands are estimated using Frequency Domain Linear Prediction (FDLP). Spectral features are derived by integrating the sub-band Hilbert envelopes in short-term frames and the temporal features are formed by converting the FDLP envelopes into modulation frequency components. These are then combined at the phoneme posterior level and are used as the input features for a phoneme recognition system. In order to improve the robustness of the proposed features to telephone speech, the sub-band temporal envelopes are gain normalized prior to feature extraction. Phoneme recognition experiments on telephone speech in the HTIMIT database show significant performance improvements for the proposed features when compared to other robust feature techniques (average relative reduction of $11\%$ in phoneme error rate).