Sentence examples for obtained emotion from inspiring English sources

From next comparison follows that obtained emotion classification error rate using only 8-parameters feature vector gives the mean value of 49.3%.

The main point of our analysis consists in testing of the influence of the used type of the feature vector on the obtained GMM emotion recognition score.

But how do we obtain an emotion lexicon annotated with emotion type, intensity, and valence?

In this paper we propose to combine speech-based and linguistic classification in order to obtain better emotion recognition results for user spoken utterances.

The present research aimed to fill in this gap of knowledge by exploring how evoked consumption contexts affect responses obtained from an emotion questionnaire.

Functional magnetic resonance imaging scans obtained during an emotion face matching task (EFMT) and an affective stimulus expectancy task (STIMEX) were performed at baseline, one hour after initial drug administration and 28 days later.

Random-effect group analyses were performed on activation maps obtained for each emotion dimension in each individual, using a repeated-measure ANOVA and one-sample t-tests at the second level.

Its purpose is to examine one by one the adaptation strategies that have been tested between sessions 3 and 4 in order to determine their advantages and drawbacks, the results obtained and the emotions felt.

Figure 15 Influence of incorrectly chosen GMM model of gender type on stability of the emotion classification; obtained scores (upper set of graphs), determined class of emotion (bottom set); feature set P3, N gmix = 6, N iter = 1200; tested sentence expressed in neutral speaking style by the male speaker (left two graphs), and by the female speaker (right graphs).

Figure 14 Influence of limited length of feature vector on stability of the GMM emotion classification process; obtained scores (upper set of graphs), determined class of emotion (bottom set); feature sets P3_8, P3_12, and P3_16, N gmix = 6, N iter = 1200; tested sentence expressed by the female speaker in joyous style.

The emotion recognizer obtains the user emotional state from the acoustics of their utterance as well as the dialogue history.