B2AI Voice: An ethically-sourced, diverse voice dataset linked to health information

ark:59853/rocrate-b2ai-voice-3.0.0

https://doi.org/10.13026/k81f-qr68

12/16/2025

The human voice contains complex acoustic markers which have been linked to important health conditions including dementia, mood disorders, and cancer. When viewed as a biomarker, voice is a promising characteristic to measure as it is simple to collect, cost-effective, and has broad clinical utility. Recent advances in artificial intelligence have provided techniques to extract previously unknown prognostically useful information from dense data elements such as images. The Bridge2AI-Voice project seeks to create an ethically sourced flagship dataset to enable future research in artificial intelligence and support critical insights into the use of voice as a biomarker of health. Here we present Bridge2AI-Voice, a comprehensive collection of data derived from voice recordings with corresponding clinical information. Bridge2AI-Voice v3.0 contains data for 833 participants across five sites in North America. Participants were selected based on known conditions which manifest within the voice waveform including voice disorders, neurological disorders, mood disorders, and respiratory disorders. The release contains data considered low risk, including derivations such as spectrograms but not the original voice recordings. Detailed demographic, clinical, and validated questionnaire data are also made available.

Yael Bensoussan, Alexandros Sigaras, Anais Rameau, Olivier Elemento, Maria Powell, David Dorr, Philip Payne, Vardit Ravitsky, Jean-Christophe Bélisle-Pipon, Ruth Bahr, Stephanie Watts, Donald Bolser, Jennifer Siu, Jordan Lerner-Ellis, Frank Rudzicz, Micah Boyer, Yassmeen Abdel-Aty, Toufeeq Ahmed Syed, James Anibal, Dona Amraei, Stephen Aradi, Kirollos Armosh, Ana Sophia Martinez, Shaheen Awan, Steven Bedrick, Helena Beltran, Alexander Bernier, Moroni Berrios, Isaac Bevers, Alden Blatter, Rahul Brito, Amy Brown, Johnathan Brown, Léo Cadillac, Selina Casalino, John Costello, Abhijeet Dalal, Iris De Santiago, Enrique Diaz-Ocampo, Amanda Doherty-Kirby, Mohamed Ebraheem, Ellie Eiseman, Mahmoud Elmahdy, Renee English, Emily Evangelista, Kenneth Fletcher, Hortense Gallois, Gaelyn Garrett, Alexander Gelbard, Anna Goldenberg, Karim Hanna, William Hersh, Jennifer Jain, Lochana Jayachandran, Kaley Jenney, Kathy Jenkins, Stacy Jo, Alistair Johnson, Ayush Kalia, Megha Kalia, Zoha Khawa, Cindy Kostelnik, Alisa Krause, Andrea Krussel, Elisa Lapadula, Genelle Leo, Justin Levinsky, Chloe Loewith, Radhika Mahajan, Vrishni Maharaj, Siyu Miao, LeAnn Michaels, Matthew Mifsud, Marian Mikhael, Elijah Moothedan, Yosef Nafii, Tempestt Neal, Karlee Newberry, Evan Ng, Christopher Nickel, Amanda Peltier, Trevor Pharr, Michaela Pnacekova, Matthew Pontell, Claire Premi-Bortolotto, Parnaz Rafatjou, JM Rahman, John Ramos, Sarah Rohde, Michael de Riesthal, Jillian Rossi, Laurie Russell, Samantha Salvi Cruz, Joyce Samuel, Suketu Shah, Ahmed Shawkat, Elizabeth Silberholz, John Stark, Lala Su, Shrramana Ganesh Sudhakar, Duncan Sutherland, Venkata Swarna Mukhi, Jeffrey Tang, Luka Taylor, Jamie Toghranegar, Julie Tu, Megan Urbano, Gavin Victor, Kimberly Vinson, Jordan Wilke, Claire Wilson, Madeleine Zanin, Xijie Zeng, Theresa Zesiewicz, Robin Zhao, Pantelis Zisimopoulos, Satrajit Ghosh

PhysioNet

Yael Bensoussan

https://physionet.org/content/b2ai-voice/view-license/3.0.0/

Limited dataset available with Data Use Agreement

voice, Voice as a biomarker, Voice dataset, Acoustic biomarker, Speech analysis, Voice recording, Human voice, Vocal health, Spectrogram, Mel spectrogram, MFCC (Mel-frequency cepstral coefficients), Fundamental frequency (F0), Phonetic posteriorgrams (PPGs), Articulatory features, Acoustic features, Pitch detection, Loudness, Periodicity

Bensoussan, Y., Sigaras, A., Rameau, A., Elemento, O., Powell, M., Dorr, D., Payne, P., Ravitsky, V., Bélisle-Pipon, J., Bahr, R., Watts, S., Bolser, D., Siu, J., Lerner-Ellis, J., Rudzicz, F., Boyer, M., Abdel-Aty, Y., Ahmed Syed, T., Anibal, J., ... Ghosh, S. (2025). Bridge2AI-Voice: An ethically-sourced, diverse voice dataset linked to health information (version 3.0.0). PhysioNet. RRID:SCR_007345. https://doi.org/10.13026/k81f-qr68

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