A sample of the human voice is a rich piece of unstructured data. Voice recordings can be turned into visualizations called spectrograms. Machine learning models can be trained to identify features of these spectrograms. Using this kind of analytic strategy, breakthroughs in voice analysis are happening at an amazing pace.

Rita Singh researches voice at Carnegie Mellon University. Her work studies the high volume of latent data that is available in the human voice. As she explains, just a small fragment of a human voice can be used to identify who a speaker is. Your voice is as distinctive as your fingerprint.

Your voice can also reveal medical conditions. Features of the human voice can be strongly correlated with psychiatric symptom severity, and potentially heart disease, cancer, and other illnesses. The human voice can even suggest a person’s physique–your height, weight, and facial features.

In this episode, Rita explains the machine learning techniques that she uses to uncover the hidden richness of the human voice.

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