Detection of cardiovascular reactivity in speech

The feasibility of automatically detecting cardiovascular reactivity from speech was investigated. There are studies that have shown success in detecting heart rate in the speech signal before but cardiovascular reactivity has not been looked at as well. Gender-specific, speaker-independent Gaussian mixture models were trained on speech during high and low cardiovascular reactivity and classification implemented using a cosine distance scoring (ivector) approach. Using five distinct criteria to determine whether classification was meaningful, we found clear indication that cardiovascular reactivity affects the voice in a manner that makes it automatically detectable in speech. As such it may become a powerful new information source for estimating various physiological conditions from speech.