The Surrey Audio-Visual Expressed Emotion (SAVEE) dataset was recorded as a pre-requisite for the development of an automatic emotion recognition system. The database consists of recordings from 4 male actors in 7 different emotions, 480 British English utterances in total. The sentences were chosen from the standard TIMIT corpus and phonetically-balanced for each emotion. The data were recorded in a visual media lab with high quality audio-visual equipment, processed and labeled. To check the quality of performance, the recordings were evaluated by 10 subjects under audio, visual and audio-visual conditions. Classification systems were built using standard features and classifiers for each of the audio, visual and audio-visual modalities, and speaker-independent recognition rates of 61%, 65% and 84% achieved respectively.