TY - JOUR
T1 - Developing Multi-Disorder Voice Protocols
T2 - 25th Interspeech Conferece 2024
AU - Bridge2AI-Voice Consortium
AU - Rameau, Anais
AU - Ghosh, Satrajit
AU - Sigaras, Alexandros
AU - Elemento, Olivier
AU - Belisle-Pipon, Jean Christophe
AU - Ravitsky, Vardit
AU - Powell, Maria
AU - Johnson, Alistair
AU - Dorr, David
AU - Payne, Philip
AU - Boyer, Micah
AU - Watts, Stephanie
AU - Bahr, Ruth
AU - Rudzick, Frank
AU - Ellis, Jordan Lerner
AU - Awan, Shaheen
AU - Bolser, Don
AU - Bensoussan, Yael
AU - Jenkins, Kathy
AU - Ramirez, Jessily
AU - Low, Daniel
AU - Toghranegar, Jamie
AU - Michaud, Gaetane
AU - Kostelnik, Cindy
AU - Farb, Ariel
AU - McCutcheon, Desiree
AU - Salvi-Cruz, Samantha
N1 - Publisher Copyright:
© 2024 International Speech Communication Association. All rights reserved.
PY - 2024
Y1 - 2024
N2 - The world of voice biomarkers is rapidly evolving thanks to the use of artificial intelligence (AI) allowing large-scale analysis of voice, speech, and respiratory sound data. The Bridge2AI-Voice project aims to build a large-scale, ethically sourced, and diverse voice database of human voices linked to health information to help fuel Voice AI research, dubbed Audiomics. The current paper describes the development of protocols of data acquisition across 4 different adult cohorts of disease (voice, respiratory, neurodegenerative diseases, mood, and anxiety disorders) using a Team Science approach for broader adoption by the research community and feedback. Demographic Surveys, Confounders Assessments, Acoustic tasks, validated patient-reported outcome (PRO) questionnaires and clinician-validated diagnostic questions were grouped in a common PART A across all cohorts and individual PART B, with cohort-specific tasks.
AB - The world of voice biomarkers is rapidly evolving thanks to the use of artificial intelligence (AI) allowing large-scale analysis of voice, speech, and respiratory sound data. The Bridge2AI-Voice project aims to build a large-scale, ethically sourced, and diverse voice database of human voices linked to health information to help fuel Voice AI research, dubbed Audiomics. The current paper describes the development of protocols of data acquisition across 4 different adult cohorts of disease (voice, respiratory, neurodegenerative diseases, mood, and anxiety disorders) using a Team Science approach for broader adoption by the research community and feedback. Demographic Surveys, Confounders Assessments, Acoustic tasks, validated patient-reported outcome (PRO) questionnaires and clinician-validated diagnostic questions were grouped in a common PART A across all cohorts and individual PART B, with cohort-specific tasks.
KW - consent
KW - disorders
KW - standardized voice protocol
KW - team science
UR - http://www.scopus.com/inward/record.url?scp=85214795911&partnerID=8YFLogxK
U2 - 10.21437/Interspeech.2024-1926
DO - 10.21437/Interspeech.2024-1926
M3 - Conference article
AN - SCOPUS:85214795911
SN - 2308-457X
SP - 1445
EP - 1449
JO - Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH
JF - Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH
Y2 - 1 September 2024 through 5 September 2024
ER -