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84 Socially Intelligent Agents

5. The top 14 features are: F0 maximum, F0 standard deviation, F0 range, F0 mean, BW1 mean, BW2
mean, energy standard deviation, speaking rate, F0 slope, F1 maximum, energy maximum, energy range,
F2 range, and F1 range.
6. The first set included the top 8 features (from F0 maximum to speaking rate), the second extended
the first by the next 2 features (F0 slope and F1 maximum), and the third included all 14 top features.
7. An ensemble consists of an odd number of neural network classifiers trained on different subsets.
The ensemble makes a decision based on the majority voting principle.
8. To train the experts, we used a two-layer backpropagation neural network architecture with a 8-
element input vector, 10 or 20 nodes in the hidden sigmoid layer and one node in the output linear layer.
We also used the same subsets of the s70 data set as training and test sets but with only two classes (for
example, angry – non-angry).
9. To explore this approach, we used a two-layer backpropagation neural network architecture with a
5-element input vector, 10 or 20 nodes in the hidden sigmoid layer and five nodes in the output linear layer.
We selected five of the best experts and generated several dozens neural network recognizers.
10. We created ensembles of 15 neural network recognizers for the 8-,10-, and 14-feature inputs and
the 10- and 20-node architectures. The average accuracy of the ensembles of recognizers lies in the range
73–77% and achieves its maximum ∼77% for the 8-feature input and 10-node architecture.

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