Voice Biomarkers - The WHY
Voice as a Biomarker of Health:
We are building an ethically sourced, bio-acoustic database to understand diseases like never before!
Background:
The human voice is often referred to as a unique print for each individual and contains biomarkers that have been linked to various diseases ranging from Parkinson’s disease to dementia, mood disorders and cancers. Voice contains complex acoustic markers that depend on the coordination between respiration, phonation, articulation, and prosody. Recent advances in acoustic analysis technology, in particular those linked to machine learning, have shed new insights into the detection of diseases. As a biomarker, voice is unique, cost-effective, easy and safe to collect in low resource settings. Moreover, the human voice not only contains speech, but also other acoustic biomarkers such as respiratory sounds, and cough.
A growing number of AI start-ups are using voice and other acoustic data, such as cough sounds, to screen for conditions such as burnout, vocal pathologies, and COVID-19, most recently. Although the preliminary results are promising, many limitations to Voice AI research remain. Presently, most available voice databases are of small size and questionable acoustic quality, lack data labelling for more than one condition, and often represent a single homogeneous population. Voice is considered a biometric identifier subject to HIPAA regulation, limiting multi-institutional collaborations due to ethical considerations – ultimately hampering the creation of accessible, robust, and diverse voice datasets.
For voice to emerge as a biomarker of health, there is a pressing need for large, high quality, multi-institutional and diverse voice database linked to other health biomarkers from various data of different modality (demographics, imaging, genomics, risk factors, etc.) to fuel voice AI research and answer tangible clinical questions. Such endeavor is only achievable through multi-institutional collaborations between voice experts and AI engineers, supported by bioethicists and social scientists to ensure the creation of ethically sourced voice databases representing our populations.
Objective of the Grand Challenge:
Our group aims to develop voice as a biomarker of health used in clinical care. To do so we will generate a large multi-institutional, ethically sourced, and diverse voice database linked to multimodal health biomarkers to fuel voice AI research. We will then build predictive models to assist in screening, diagnosis, and treatment of a broad range of diseases, including several diseases with unmet clinical needs. Data collection will be made possible via the development of cutting-edge software available as smartphone application linked to electronic health records (EHR). Data collection will be combined with other health biomarkers such as radiomics, and genomics. Importantly, this project will pioneer the use of federated learning technology to create multi-center machine learning models while strictly protecting data privacy. Rising ethical concerns regarding Voice AI such as legal implications of voice identification, voice AI hacking and voice data sharing and privacy, and impact of gender and racial diversity on Voice AI will be addressed.
Based on the existing literature and ongoing research in different fields of voice research, our group has identified 5 disease cohort categories for which voice changes have been associated to specific diseases with well-recognized unmet needs. We will center our data acquisition efforts on the following disease categories:
Voice Disorders: (Laryngeal cancers, vocal fold paralysis, benign laryngeal lesions)
Neurological and Neurodegenerative Disorders: (Alzheimer’s, Parkinson’s, Stroke, ALS)
Mood and Psychiatric Disorders: (Depression, Schizophrenia, Bipolar Disorders
Respiratory disorders: (Pneumonia, COPD, Heart Failure)
Pediatric Voice and Speech Disorders: (Speech and language delays, Autism)
The data voice data acquisition efforts will be facilitated by partnerships with High Volume Expert Clinics as well as Community Clinics representing underserved populations. In addition to the data acquisition around the 5 disease categories, we aim to partner with large existing cohorts to acquire voice data of a large-scale diverse population as a control.
As Voice is increasingly being recognized as a biomarker of health by the tech world and Voice AI is gaining attention from multi-nationals such as Google, Amazon, Mozilla and Apple amongst others, many important issues related to patient privacy protection, ethical and fair representation of population, and clinical accuracy are arising. As a multidisciplinary group of academic experts, we aim to influence and guide the world of Voice AI by ensuring patient protection through ethical and fairness principles and create safe innovative infrastructures to disseminate ethically sourced data for the future generations of Voice AI researchers.