In this paper we study the relevance of so called high-level speech features for the application of speaker independent emotion recognition. After we give a brief definition of high-level features, we discuss for which standard feature groups high-level features are conceivable. Two groups of high-level features are proposed within this paper: a feature set for the parametrization of phonation called voice quality parameters and a second feature set deduced from music theory called harmony features. Harmony features give information about the frequency interval and chord content of the pitch data of a spoken utterance. Finally, we study the gain in classification rate by combining the proposed high-level features with the standard low-level features. We show that both high-level feature sets improve the speaker independent classification performance for spontaneous emotional speech.