Sentence examples for emotional classification from inspiring English sources

The relationship between a particle's position and emotional classification is also defined.

As the shape of the vocal tract depends also on the emotional state of the speaker, these coefficients can be used in the feature vector for GMM emotional classification.

The emotional classification task during the scan was kept the same in the two experiments to make sure that any difference between the results of the two experiments could not be explained by difference in behavioural tasks.

Automatic emotion classification experiments were carried out on the Linguistic Data Consortium emotional prosody speech and transcripts corpus and the FAU Aibo corpus to validate the proposed approach.

Next, emotional values of 50 users in 10 time periods are selected to make emotion classification analysis for micro-blog users by using the fuzzy clustering algorithm, and F testing method is used to calculate an optimal classification.

These values are both higher than the human recognition efficiencies cited in [1] (η = 3.72 and η = 3.55 for emotional state classification based on original knocking motions in [35] (four emotions, point-light display) and [36] (five emotions, full video)), indicating that the style component was accurately perceived in both our original and synthesized sequences.

The filter banks obtained with the proposed methodology improve the results in stressed and emotional speech classification.

This article analyzes and compares influence of different types of spectral and prosodic features for Czech and Slovak emotional speech classification based on Gaussian mixture models (GMM).

The research framework is shown in Fig. 1 and includes 4 steps: (1) data collection and preprocessing, (2) subjective sentence identification, emotional trend classification, and object description extraction, (3) the object library of the financial reports, and (4) empirical analysis.

Obtained results of the first experiment with a cascade connection of the GMM gender recognition block and the emotional style classification block (see Figure1) show that this approach is applicable and the obtained recognition error of the whole GMM classifier presented in Table 16 achieves acceptable values (the mean error rate for all four emotions and both voices is 21.13%).

Using hierarchical agglomerative clustering of the photographs, we obtained (1) six visual and emotional landscape classifications composed by each group of respondents and (2) a general visual and emotional landscape classification that comprises the 'collective' opinion of the Russian and Japanese respondents.