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80 Socially Intelligent AgentsTable 9.2.Evaluators’ and Actors’ statistics.Evaluators’ statisticsActors’ statisticsCategory Mean s.d. Median Min Max Mean s.d. Median Min MaxNormal 66.3 13.7 64.3 29.3 95.7 65.1 16.4 68.5 26.1 89.1Happy 61.4 11.8 62.9 31.4 78.6 59.8 21.1 66.3 2.2 91.3Angry 72.2 5.3 72.1 62.9 84.3 71.7 24.5 78.2 13.0 100Sad 68.3 7.8 68.6 50.0 80.0 68.1 18.4 72.6 32.6 93.5Afraid 49.5 13.3 51.4 22.1 68.6 49.7 18.6 48.9 17.4 88.0the following number of items: s70: 369 utterances or 52.0% of the corpus;s80: 257/36.7%; s90: 149/21.3%; s95: 94/13.4%; and s100: 55/7.9%. Wecan see that only 7.9% of the utterances of the corpus were recognized byall subjects, and this number lineally increases up to 52.7% for the data sets70, which corresponds to the 70% level of concordance in decoding emotionin speech. Distribution of utterances among emotion categories for the datasets is close to a uniform distribution for s70 with ∼20% for normal state andhappiness, ∼25% for anger and sadness, and 10% for fear. But for data setswith higher level of concordance anger begins to gradually dominate while theproportion of the normal state, happiness and sadness decreases. Interestingly,the proportion of fear stays approximately at the same level (∼7–10%) forall data sets. The above analysis suggests that anger is easier to portray andrecognize because it is easier to come to a consensus about what anger is.3.3 Feature ExtractionAll studies in the field point to pitch (fundamental frequency) as the mainvocal cue for emotion recognition. Other acoustic variables contributing tovocal emotion signaling are [1]: vocal energy, frequency spectral features, formants(usually only one or two first formants (F1, F2) are considered), andtemporal features (speech rate and pausing). Another approach to feature extractionis to enrich the set of features by considering some derivative featuressuch as LPCC (linear predictive coding cepstrum) parameters of signal [12] orfeatures of the smoothed pitch contour and its derivatives [5].For our study we estimated the following acoustic variables: fundamentalfrequency F0, energy, speaking rate, and first three formants (F1, F2, and F3)and their bandwidths (BW1, BW2, and BW3), and calculated some descriptivestatistics for them 3 . Then we ranked the statistics using feature selection techniques,and picked a set of most “important” features. We used the RELIEF-Falgorithm [8] for feature selection 4 and identified 14 top features 5 . To investigatehow sets of features influence the accuracy of emotion recognitionalgorithms we formed 3 nested sets of features based on their sum of ranks 6 .