Voice can tell a lot of information about our health.
We believe that voice is the new blood for medical diagnosis.
Voice is what makes us human
Every sentence that we say contains a rich array of information about yourselves, our age, our gender, our emotions and our health status. The simple act of speaking requires engaging our brain, lungs and coordinating more than 100 muscles. By combining sound analysis with artificial intelligence and deep learning, VoiceMed detects subtle signals in the sounds produced by the vocal apparatus.
These signals are called vocal biomarkers. There is a large body of research supporting the application of vocal biomarkers to diagnosis, screening and monitoring of diseases. The field is most advanced in diseases of the brain and the nervous system [1,2,3] the respiratory system  and mental disorders .
For respiratory diseases, in addition to speech , other sounds such as breathing and coughing can provide important information about one’s health .
What are Vocal Biomarkers?
Biomarker is a characteristic that indicates normal biological and pathogenic processes that may include molecular, histological, radiographic, or physiologic characteristics.
The advent of digital devices for healthcare setting has paved the way for defining digital biomarkers as “objective, quantifiable, physiological, and behavioral measures that are collected by means of digital devices that are portable, wearable, implantable, or digestible”.
Though a formal definition of vocal biomarker is yet to be framed, we define vocal biomarker as a specific characteristic of voice that indicates the underlying physiological and/or pathophysiological condition.
 Zhang, H., Song, C., Rathore, A.S., Huang, M., Zhang, Y., Xu, W., 2020. mHealth Technologies towards Parkinson’s Disease Detection and Monitoring in Daily Life: A Comprehensive Review. IEEE Reviews in Biomedical Engineering 1–1.
 Â Kourtis, L.C., Regele, O.B., Wright, J.M., Jones, G.B., 2019. Digital biomarkers for Alzheimer's disease: In the mobile/wearable devices opportunity.npj Digital Medicine 2, 1-9.
 Yu, B., Quatieri, T., Williamson, J., Mundt, J., n.d. Cognitive Impairment Prediction in the Elderly Based on Vocal Biomarkers, in: INTERSPEECH-2015. Presented at the INTERSPEECH-2015, pp. 3734–3738.
 Palaniappan, R., Sundaraj, K., Sundaraj, S., 2014. Artificial intelligence techniques used in respiratory sound analysis – a systematic review. Biomedical Engineering / Biomedizinische Technik 59, 7–18.
 Low, D.M., Bentley, K.H., Ghosh, S.S., 2020. Automated assessment of psychiatric disorders using speech: A systematic review. Laryngoscope Investigative Otolaryngology 5, 96–116.
 Lee, L., Loudon, R.G., Jacobson, B.H., Stuebing, R., 1993. Speech breathing in patients with lung disease. Am Rev Respir Dis 147, 1199–1206
 Tracey, B.H., Comina, G., Larson, S., Bravard, M., López, J.W., Gilman, R.H., 2011. Cough detection algorithm for monitoring patient recovery from pulmonary tuberculosis, in: 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 6017–6020.