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We present a new feature extraction technique for phoneme recognition that uses short-term spectral envelope and modulation frequency features. These features are derived from sub-band temporal envelopes of speech estimated using Frequency Domain Linear Prediction (FDLP). While spectral envelope features are obtained by the short-term integration of the sub-band envelopes, the modulation frequency components are derived from the long-term evolution of the sub-band envelopes. These features are combined at the phoneme posterior level and used as features for a hybrid HMM-ANN phoneme recognizer. For the phoneme recognition task on the TIMIT database, the proposed features show an improvement of 4.7% over the other feature extraction techniques.