Tales of tuning - prototyping for automatic classification of emotional user states

Classification performance for emotional user states found in the few realistic, spontaneous databases available is as yet not very high. We present a database with emotional children’s speech in a human-robot scenario. Baseline classification performance for seven classes is 44.5%, for four classes 59.2%. We discuss possible strategies for tuning, e.g., using only prototypes (based on annotation correspondence or classification scores), or taking into account requirements and feasibility in possible applications (weighting of false alarms or speakerspecific overall frequencies).