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The Bridge2Ai Voice Consortium is composed of Voice AI researchers with various backgrounds and from many different institutions across the US and CANADA.
The group is composed of clinicians, data engineers, AI experts, bio-ethicists, speech pathologists, acoustic engineers and educators that have the common goal of building a large human voice database related to health information and disseminating data and findings to improve patient outcomes.
The consortium’s work is centered around people, ethics, and data in order to develop the standardized methods of ethical voice data collection, and to develop the resources and infrastructure to train the future generation of AI researchers.
University of South Florida
Weil Cornell
AIM: To build bridges between the medical voice research world, the acoustic engineers, and the AI/ML world to promote algorithms with the integration of tangible clinical application for Voice AI algorithms
AIM: To integrate existing scholarship, tools, and guidance with development of new standard and normative insights for identifying, anticipating, addressing, and providing guidance on ethical and trustworthy issues from voice data generation and AI/ML research and development to clinical adoption and downstream health decisions and outcomes. To develop new guidelines for consenting to voice data collection, voice data sharing and utilization in the context of voice AI technology.
Simon Fraiser University
President and CEO of The Hastings Center
AIM: To introduce the field of acoustic biomarkers by developing new standards of acoustic and voice data collection and analysis for voice AI research.
Sickkids
MIT
Sickkids
AIM: To develop a software and cloud infrastructure for automated voice data collection through a smartphone application that allows non-invasive, user-friendly, high quality voice data collection while minimizing human manipulation. This will include integrated acoustic amplifiers and acoustic quality standardization. To implement Federated Learning technology to allow analysis of multi-institutional data while minimizing data sharing and preserving patient privacy.
Weil Cornell
Weil Cornell
Weil Cornell
AIM: To build a multi-modal, multi-institutional, large scale, diverse and ethically sourced human voice database linked to other biomarkers of health that is AI/ML friendly to fuel voice AI research
University of South Florida
Weil Cornell Medicine
MIT
Weil Cornell Medicine
University of South Florida
University of Florida
University of South Florida
University of Toronto
University of South Florida
University of South Florida
AIM: To develop a unique curriculum on voice biomarkers of health and the development, validation, and implementation of AI models that are FAIR and CARE – To create a community of voice AI researchers, especially those from underserved communities, and foster collaborations to promote application of ML for Voice Research – To engage a broad range of learners with competency assessment and mentorship.
Oregon Health & Science University
Washington University School of Medicine in St. Louis
Oregon Health & Science University
Oregon Health & Science University
Washington University School of Medicine in St. Louis
AIM: To review and implement diversity and inclusion throughout the project’s demographics and in the distribution of the data results researched.
Vanderbilt University Medical Center
University of Texas Medical Center
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