Zyto Insights Wellness Voice Scan




 Transform your life with a simple and quick voice scan you can do from your smart phone! 

More about Voice Scan technology... 
A new hot topic in medical engineering research is disease and health detection via voice analysis.
 
There is growing research suggesting that voice analysis can help to assess health and diagnose certain diseases, and there is now a rush to develop technology that can do just that.
 
Voice analysis technology is of such high interest because it has the potential to be a reliable, efficient, affordable, convenient, and easy-to-use method to predict, diagnose, and support health concerns.1 
 
How does voice analysis technology work?
Research is finding that not just what we say, but how we say it, might give us clues about our state of health.
 
It turns out that certain mental and physical health conditions are associated with changes in how you talk—such as variation in tone, rate of speech, slurring of words, emphases, length of pauses, and much more. Technologies are being developed that aim to predict and diagnose a wide variety of health conditions based on these kinds of factors.1 2 3
 
These technologies extract specific voice and acoustic features (referred to as vocal biomarkers) from audio voice samples using various techniques. Then, those features are analyzed for important patterns and cues to provide insights about your health.1 2
 
Over time, researchers hope to identify specific vocal biomarkers that can help diagnose, predict, and monitor certain health conditions.4
 
Along with potentially being able to detect a wide variety of diseases and health conditions, voice analysis is already being used for preventative wellness and providing other individualized health & wellness information.
 
In fact, computerized analysis of acoustic vocal patterns to predict and manage states of health dates back more than 30 years. Some of the pioneers in this field include Dr. Sharry Edwards and Dr. Dorinne Davis.13 14
 
Expanding on the work of these earlier pioneers, ZYTO founder Dr. Vaughn Cook developed the Zyto Insights Voice Scan system, which maps and analyzes a person’s voice when they speak about a specific topic. Through a patented biofeedback process, Zyto Insights is able to provide to a person the information of the “missing” frequencies in their voice. The Zyto Insights Voice Scan uses two main biometric characteristics (kinetic touch and voice analysis) to get your unique digital profile and use that information to compare to the scans virtual items to determine which products would be best matched to meet your wellness needs at the time of the scan. .
 
While ZYTO and other pioneers are already using this technology to assist with holistic health, there are now numerous companies, organizations, and startups getting in on the exciting scene of voice analysis—from IBM to the US Army. 

This technology is already being successfully applied in a number of different medical and holistic health fields.
 
As the development of the technology progresses, voice analysis could introduce more efficiency, accuracy, convenience, and affordability to diagnosing, monitoring, and supporting various health conditions.

The main feedback I have gotten from those who have taken the scan is they are blown away with how accurate it was to how they felt.  What have you got to lose?  If you are interested in doing the scan, just CLICK HERE  For a limited time this is a FREE offer!


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Sources:
1. Saloni, R.K. Sharma, & A.K. Gupta. “Disease detection using voice analysis: a review.” International Journal of Medical Engineering and Informatics 6, no. 3 (2014): 189-209.
2. Place, S., D. Blanch-Hartigan, C. Rubin, et al. “Behavioral Indicators on a Mobile Sensing Platform Predict Clinically Validated Psychiatric Symptoms of Mood and Anxiety Disorders.” Journal of Medical Internet Research 19, no. 3 (2017): e75.
3. Arevian, A.C., D. Bone, N. Malandrakis, et al. “Clinical state tracking in serious mental illness through computational analysis of speech.” PLOS ONE 15, no. 1 (2020): e0225695.
5. Campisi, P., T.L Tewfik, J.J. Manoukian, et al. “Computer-Assisted Voice Analysis Establishing a Pediatric Database.” Arch Otolaryngology Head Neck Surg 128, no. 2 (2002): 156-160.
6. “AI in mental health screening: Voice analysis shows promise.” Healthline Media UK Ltd, Brighton, UK. Medicalnewstoday.com.
7. “Vision.” Parkinson’s Voice Initiative. Parkinsonsvoice.org.
8. Moro-Velázquez, L, J.A. Gómez-García, J.I. Godino-Llorente, et al. “Analysis of speaker recognition methodologies and the influence of kinetic changes to automatically detect Parkinson’s Disease.” Applied Soft Computing 62 (2018): 649-666.
9. Marmar, C.R., A.D. Brown, M. Qian, et al. “Speech‐based markers for posttraumatic stress disorder in US veterans.” Depression & Anxiety 36, no. 7 (2019): 607-616.
10. Mota, N.B., N.A.P. Vasconcelos, N. Lemos, et al. “Speech Graphs Provide a Quantitative Measure of Thought Disorder in Psychosis.” PLOS ONE 7, no. 4 (2012): e34928.
11. Maor, E., J.D. Sara, D. M. Orbelo, et al. “Voice Signal Characteristics Are Independently Associated With Coronary Artery Disease.” May Clinic Proceedings 93, no. 7 (2019): 840-847.
12. Chan, J., T. Rea, S. Gollakota, & J.E. Sunshine. “Contactless cardiac arrest detection using smart devices.” npj Digital Medicine 2 (2019): 52.
14. Davis, Dorinne S. The Cycle of Sound: A Missing Energetic Link. (New Pathways Press, 2012).
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