Sentence examples for emotion classification from inspiring English sources

This test compared the obtained CPU times for GMM creation and training phase in both genders, as well as the CPU times of emotion classification phases (neutral and three emotional styles for male/female gender).

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.

The proposed method improves the sensitivity, specificity and Gm of emotion classification compared with the typical classification methods.

Incorrectly chosen GMM model of gender type which is subsequently applied for emotion classification has no influence on stability but practically causes large error rate of emotion classification.

Affect or emotion classification from speech has much to benefit from ensemble classification methods.

stability of the GMM emotion classification process with the limited length of the feature vector;   3.

Figure 3 Emotion classification based on dialogue features (blue = depth, red = width).

influence of different length of the feature vector on GMM emotion classification error rate;   4.

Channel selection approaches adopted for emotion classification can be categorized to filtering and wrapper techniques.

Ghose et al. researchers started to apply the LingPipe for emotion classification.