Klick Scientists See New Vocal Patterns Associated with Type 2 Diabetes

Scientists at Klick Labs have created visual maps of sound frequencies (spectrograms) to identify complex, new patterns associated with Type 2 diabetes, using only smartphone recordings of randomly selected short English sentences and participant sex as study criteria.

The scientists analyzed 14,759 voice recordings from 461 adults, which were converted into a visual spectrogram before being analyzed by an audio spectrogram transformer. The process allows the model to learn complex combinations of acoustic patterns directly from the recordings rather than relying primarily on predefined voice features selected in advance. Using voice only, the sex-stratified models achieved a balanced accuracy of 71.3 percent for women and 72.4 percent for men, with area under the receiver operating characteristic curve (AUC) values of 0.788 (78.8 percent) and 0.779 (77.9 percent), respectively.

"Now, we can literally see many more clues related to diabetes within vocal biomarkers," said Yan Fossat, senior vice president of digital health research and development at Klick Applied Sciences and principal investigator of the study, in a statement. "By capturing relationships across frequencies and time, we expanded our analysis beyond shimmer, jitter, and other predefined voice features and found complex combinations of acoustic patterns and features that weren't on anyone's radar. We are energized by the findings as we continue to explore associations between vocal biomarkers and Type 2 diabetes."

Klick's latest research builds on a 2023 Klick study that analyzed fixed one-sentence smartphone voice recordings alongside participants' age, body mass index, and other basic health information. The study identified 14 predefined acoustic features, including pitch, intensity, shimmer, and jitter, associated with Type 2 diabetes.