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Apart from the usually employed MFCC, PLP, and energy feature information, also duration, low order formants, pitch, and center-of-gravity-based features are known to carry valuable information for phoneme recognition. This work investigates their individual performance within segment-based acoustic modeling. Also, experiments optimizing a feature space spanned by this set, exclusively, are reported, using CFSS feature space optimization and speaker adaptation. All tests are carried out with SVM on the open IFA-corpus of 47 Dutch hand-labeled phonemes with a total of 178k instances. Extensive speaker dependent vs. independent test-runs are discussed as well as four different speaking styles reaching from informal to formal: informal and retold story telling, and read aloud with fixed and variable content. Results show the potential of these rather uncommon features, as e.g. based on F3 or pitch.