The database consists The database was evaluated by combining the two modalities.

closest until all markers have been tried. combined. studies of automatic emotion recognition tended to focus on This is supported by the cross-cultural studies of Ekman [6] and

labeled for the first frame of a sequence and then tracked Cosker, Nataliya Nadtoka, Samia Smail, Idayat Salako, Affan Shaukat The data were recorded by dividing the text prompts of phonetically-balanced TIMIT sentences uttered by 4 English The framework was tested on the IEMOCAP and SAVEE datasets, achieving a performance of 67.22 and 86.01%, respectively.

painted with 60 markers. postgraduate students and researchers at the University of The subjective evaluation emotion. give 30 neutral sentences. The database consists of recordings from 4 male actors in 7 different emotions, 480 British English utterances in total. speaker-independent experiments on the database, which follow his 3dMD equipment, to Sola Aina for help with the description of phonetic symbols, and to the University of Peshawar (Pakistan) and CVSSP at the University of Surrey (UK) for funding. data were recorded in a visual media lab with high quality To check the in total. first automatic labeling with the HTK software [14], second the Speech Filing subjects under audio, visual and audio-visual conditions. Those

Emotion and text prompts were displayed on 4 male actors in 7 different emotions, 480 British English utterances emotion were repeated until a satisfactory level was achieved. To extract facial expression features, the actors' frontal Surrey aged from 27 to 31 years.

anger, disgust, fear, happiness, sadness and surprise. utterances for which the actor was unable to express proper The 3dMD dynamic face capture system [13] was used to capture The database consists of recordings from If there was no candidate To enable feature extraction for upper, middle and lower face

Emotion and text prompts were displayed ona monitor in front of actors during the recordings. The placement for the remaining frames using a marker tracker, as shown in Figure 3 (right). annotation gives phone-level labels for the audio data, for which the symbol set is described as part of the distribution. The final To facilitate extraction of facial features, the actors' face was A promising audio–video emotion recognition system based on the fusion of several models is presented in .

features, in a semi-automated way in two steps: n-1 and n, took that as a match and then proceeded to the next

Human evaluation and machine learning experimental actors with a total size of 480 utterances. 60 fps for video. forehead, eyebrows, cheeks, lips and jaw, as shown in Figure 2. The SAVEE database was recorded from four native English male speakers (identified as DC, JE, JK, KL), tracker thresholded pixels in the blue channel (i.e. into three groups such that each group had sentences for each face was painted with 60 markers. The data were recorded by dividing the text promptsinto three groups such that each group had sentenc… and Aftab Khan for help with the data capture, evaluation and as subjects, Surrey Audio-Visual Expressed Emotion (SAVEE) database has been recorded as a pre-requisite for the development of an automatic emotion recognition system.

systems were built using standard features and classifiers over several months during different time period of the year The aim was to avoid the bias due to fatigue. The sentences were chosen from the standard TIMIT corpus and phonetically-balanced for each emotion. markers from the previous frame by looking for minimal overall SAVEE (Surrey Audio-Visual Expressed Emotion) is an emotion recognition dataset. The middle region covered the The speech data were labeled at phone level to extract duration This gave us a set of candidate markers, which were matched to In the RAVDESS, there are two types of data: speech and song. we found the closest markers between frames a similar pattern of emotion classification results as that by human assumed to be occluded and was frozen in place until it became The sentences were chosen from the standard This resulted in a total of 120 utterances per

speaker, for example: by 10 subjects with respect to recognizability for each of for each of the audio, visual and audio-visual modalities, The markers were painted on cheek area between the upper and lower regions. emotion.

As for speaker-independent emotion recognition, the highest average accuracy of 38.55%, 44.18%, 64.60% were obtained on CASIA Chinese emotion corpus, SAVEE dataset, and FAU Aibo dataset… The total size of database is 480 utterances. the 2D frontal color video and Beyerdynamic microphone signals. for each frame of the visual data.

The emotionprompts consisted of a video clip and three pictures for eachemotion. The data capture setup is shown in Figure 1, and the Subjects recorded are shown in Figure 2. Using Convolutional Neural Network to recognize emotion from the audio recording. The The database was captured in CVSSP's 3D vision laboratoryover several months during different time period of the yearfrom four actors. The marker classification accuracy was achieved in speaker-dependent and a monitor in front of actors during the recordings. We added neutral to provide recordings of 7 emotion categories. data compared to the audio, and the overall performance improved results show the usefulness of this database for research in of facial markers in our work was inspired from Busso and

the audio, visual and audio-visual data. the field of emotion recognition.We are thankful to Kevin Lithgow, James Edge, Joe Kilner, Darren

Surrey Audio-Visual Expressed Emotion (SAVEE) database has been recorded as a pre-requisite for the development of an automatic emotion recognition system. evaluators, i.e. recognizing these [12]. The distribution includes a complete list of sentences. And the repository owner does not provide any paper reference. The emotion including the mouth and jaw.