Affective Archives project will create an oral history archive that will also collect breathing and heart rate information. In the automatic emotion analysis literature, breathing patterns and heart rate information have become a new research focus to detect emotionally speech patterns. However the lack of ground truth breathing information makes them hard to use, as manual breathing annotations are time costly, and humans performance is not accurate enough. Ground-truth is a term that describes the goal of an AI model. For example, when training an AI model to predict the breathing signal, given the interview audio recording, the ground truth will be the breathing signal recorded during the interview. The ground truth is used to train better prediction models and to evaluate how good the predictions are. As such, this archive will contribute to this literature as being the first multilingual emotional speech data collection with breathing and heart rate ground truths.