A multi-modal framework for emotion recognition using bag-of-words features and undirected, replicated softmax topic models is proposed here. Topic models ignore the temporal information between features, allowing them to capture the complex structure without a brute-force collection of statistics. Experiments are performed over face, speech and language features extracted from the USC IEMOCAP database. Performance on facial features yields an unweighted average recall of 60.71%, a relative improvement of 8.89% over state-of-the-art approaches. A comparable performance is achieved when considering only speech (57.39%) or a fusion of speech and face information (66.05%). Individually, each source is shown to be strong at recognizing either sadness (speech) or happiness (face) or neutral (language) emotions, while, a multi-modal fusion retains these properties and improves the accuracy to 68.92%. Implementation time for each source and their combination is provided. Results show that a turn of 1 second duration can be classified in approximately 666.65ms, thus making this method highly amenable for real-time implementation.