The Consortium (Who We Are)

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.

Structure

Teaming

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

Standards

AIM: To introduce the field of acoustic biomarkers by developing new standards of acoustic and voice data collection and analysis for voice AI research.

Alex Bennett MASc

FHIR

Sickkids

Tools Development and Optimization

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.

Data Acquisition

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

Satrajit Ghosh, PhD

Mood Disorders Cohort

MIT

Robin Zhao

Data Manager

Weil Cornell Medicine

Skills and Workforce Development

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.