SOURCE METADATA
Project: VOICE
Source ID: physionet_pediatric_1_1_0
Source type: data resource
Source URL: https://physionet.org/content/b2ai-voice-pediatric/1.1.0/
Raw file: data/raw/VOICE/physionet_b2ai-voice-pediatric_1.1.0_2026-07-24.html
--------------------------------------------------------------------------------
Bridge2AI-Voice Pediatric Dataset v1.1.0
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Bridge2AI-Voice Pediatric Dataset
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
,
Dona Amraei
,
James Anibal
,
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
,
Omar Ghaffar
,
Anna Goldenberg
,
Karim Hanna
,
William Hersh
,
Jennifer Jain
,
Lochana Jayachandran
,
Kaley Jenney
,
Kathy Jenkins
,
Stacy Jo
,
Alistair Johnson
,
Ayush Kalia
,
Megha Kalia
,
Zoha Khawa
,
Kenji Kobayashi
,
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
,
Jaiden Potter
,
Claire Premi-Bortolotto
,
Parnaz Rafatjou
,
JM Rahman
,
Gayathiri Rajkumar
,
John Ramos
,
Michael de Riesthal
,
Sarah Rohde
,
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
Published: May 1, 2026. Version:
1.1.0
Raw Audio Data Access for Bridge2AI Voice Pediatric Cohort is via Synapse
(March 9, 2026, 10:07 a.m.)
The published Bridge2AI-Voice Pediatric Dataset contains derived features from the audio waveforms. This PhysioNet project does not contain raw audios.
Accessing raw audio is a more involved process and requires institutional sign off. Please reach out to the access committee if you are interested in access: DACO@b2ai-voice.org
Data will be made available via Synapse
:
https://www.synapse.org/Synapse:syn73617068
For questions regarding the dataset itself, please contact the corresponding author, listed on the sidebar.
Note that the Bridge2AI-Voice Adult Dataset is also available on PhysioNet:
https://physionet.org/content/b2ai-voice/
When using this resource, please cite:
Cite
Copy BibTeX
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., Amraei, D., ... Ghosh, S. (2026). Bridge2AI-Voice Pediatric Dataset (version 1.1.0).
PhysioNet
. RRID:SCR_007345.
https://doi.org/10.13026/h995-bt35
@article{PhysioNet-b2ai-voice-pediatric-1.1.0,
author = {Bensoussan, Yael and Sigaras, Alexandros and Rameau, Anais and Elemento, Olivier and Powell, Maria and Dorr, David and Payne, Philip and Ravitsky, Vardit and Bélisle-Pipon, Jean-Christophe and Bahr, Ruth and Watts, Stephanie and Bolser, Donald and Siu, Jennifer and Lerner-Ellis, Jordan and Rudzicz, Frank and Boyer, Micah and Abdel-Aty, Yassmeen and {Ahmed Syed}, Toufeeq and Amraei, Dona and Anibal, James and Aradi, Stephen and Armosh, Kirollos and Martinez, Ana Sophia and Awan, Shaheen and Bedrick, Steven and Beltran, Helena and Bernier, Alexander and Berrios, Moroni and Bevers, Isaac and Blatter, Alden and Brito, Rahul and Brown, Amy and Brown, Johnathan and Cadillac, Léo and Casalino, Selina and Costello, John and Dalal, Abhijeet and {De Santiago}, Iris and Diaz-Ocampo, Enrique and Doherty-Kirby, Amanda and Ebraheem, Mohamed and Eiseman, Ellie and Elmahdy, Mahmoud and English, Renee and Evangelista, Emily and Fletcher, Kenneth and Gallois, Hortense and Garrett, Gaelyn and Gelbard, Alexander and Ghaffar, Omar and Goldenberg, Anna and Hanna, Karim and Hersh, William and Jain, Jennifer and Jayachandran, Lochana and Jenney, Kaley and Jenkins, Kathy and Jo, Stacy and Johnson, Alistair and Kalia, Ayush and Kalia, Megha and Khawa, Zoha and Kobayashi, Kenji and Kostelnik, Cindy and Krause, Alisa and Krussel, Andrea and Lapadula, Elisa and Leo, Genelle and Levinsky, Justin and Loewith, Chloe and Mahajan, Radhika and Maharaj, Vrishni and Miao, Siyu and Michaels, LeAnn and Mifsud, Matthew and Mikhael, Marian and Moothedan, Elijah and Nafii, Yosef and Neal, Tempestt and Newberry, Karlee and Ng, Evan and Nickel, Christopher and Peltier, Amanda and Pharr, Trevor and Pnacekova, Michaela and Pontell, Matthew and Potter, Jaiden and Premi-Bortolotto, Claire and Rafatjou, Parnaz and Rahman, JM and Rajkumar, Gayathiri and Ramos, John and {de Riesthal}, Michael and Rohde, Sarah and Rossi, Jillian and Russell, Laurie and {Salvi Cruz}, Samantha and Samuel, Joyce and Shah, Suketu and Shawkat, Ahmed and Silberholz, Elizabeth and Stark, John and Su, Lala and Sudhakar, Shrramana Ganesh and Sutherland, Duncan and {Swarna Mukhi}, Venkata and Tang, Jeffrey and Taylor, Luka and Toghranegar, Jamie and Tu, Julie and Urbano, Megan and Victor, Gavin and Vinson, Kimberly and Wilke, Jordan and Wilson, Claire and Zanin, Madeleine and Zeng, Xijie and Zesiewicz, Theresa and Zhao, Robin and Zisimopoulos, Pantelis and Ghosh, Satrajit},
title = {{Bridge2AI-Voice Pediatric Dataset}},
journal = {{PhysioNet}},
year = {2026},
month = may,
note = {Version 1.1.0},
doi = {10.13026/h995-bt35},
url = {https://doi.org/10.13026/h995-bt35}
}
Cite
×
MLA
Bensoussan, Yael, et al. "Bridge2AI-Voice Pediatric Dataset" (version 1.1.0).
PhysioNet
(2026). RRID:SCR_007345.
https://doi.org/10.13026/h995-bt35
APA
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., Amraei, D., ... Ghosh, S. (2026). Bridge2AI-Voice Pediatric Dataset (version 1.1.0).
PhysioNet
. RRID:SCR_007345.
https://doi.org/10.13026/h995-bt35
Chicago
Bensoussan, Yael, Sigaras, Alexandros, Rameau, Anais, Elemento, Olivier, Powell, Maria, Dorr, David, Payne, Philip, Ravitsky, Vardit, Bélisle-Pipon, Jean-Christophe, Bahr, Ruth, Watts, Stephanie, Bolser, Donald, Siu, Jennifer, Lerner-Ellis, Jordan, Rudzicz, Frank, Boyer, Micah, Abdel-Aty, Yassmeen, Ahmed Syed, Toufeeq, Amraei, Dona, Anibal, James, Aradi, Stephen, Armosh, Kirollos, Martinez, Ana Sophia, Awan, Shaheen, Bedrick, Steven, Beltran, Helena, Bernier, Alexander, Berrios, Moroni, Bevers, Isaac, Blatter, Alden, Brito, Rahul, Brown, Amy, Brown, Johnathan, Cadillac, Léo, Casalino, Selina, Costello, John, Dalal, Abhijeet, De Santiago, Iris, Diaz-Ocampo, Enrique, Doherty-Kirby, Amanda, Ebraheem, Mohamed, Eiseman, Ellie, Elmahdy, Mahmoud, English, Renee, Evangelista, Emily, Fletcher, Kenneth, Gallois, Hortense, Garrett, Gaelyn, Gelbard, Alexander, Ghaffar, Omar, Goldenberg, Anna, Hanna, Karim, Hersh, William, Jain, Jennifer, Jayachandran, Lochana, Jenney, Kaley, Jenkins, Kathy, Jo, Stacy, Johnson, Alistair, Kalia, Ayush, Kalia, Megha, Khawa, Zoha, Kobayashi, Kenji, Kostelnik, Cindy, Krause, Alisa, Krussel, Andrea, Lapadula, Elisa, Leo, Genelle, Levinsky, Justin, Loewith, Chloe, Mahajan, Radhika, Maharaj, Vrishni, Miao, Siyu, Michaels, LeAnn, Mifsud, Matthew, Mikhael, Marian, Moothedan, Elijah, Nafii, Yosef, Neal, Tempestt, Newberry, Karlee, Ng, Evan, Nickel, Christopher, Peltier, Amanda, Pharr, Trevor, Pnacekova, Michaela, Pontell, Matthew, Potter, Jaiden, Premi-Bortolotto, Claire, Rafatjou, Parnaz, Rahman, JM, Rajkumar, Gayathiri, Ramos, John, de Riesthal, Michael, Rohde, Sarah, Rossi, Jillian, Russell, Laurie, Salvi Cruz, Samantha, Samuel, Joyce, Shah, Suketu, Shawkat, Ahmed, Silberholz, Elizabeth, Stark, John, Su, Lala, Sudhakar, Shrramana Ganesh, Sutherland, Duncan, Swarna Mukhi, Venkata, Tang, Jeffrey, Taylor, Luka, Toghranegar, Jamie, Tu, Julie, Urbano, Megan, Victor, Gavin, Vinson, Kimberly, Wilke, Jordan, Wilson, Claire, Zanin, Madeleine, Zeng, Xijie, Zesiewicz, Theresa, Zhao, Robin, Zisimopoulos, Pantelis, and Satrajit Ghosh. "Bridge2AI-Voice Pediatric Dataset" (version 1.1.0).
PhysioNet
(2026). RRID:SCR_007345.
https://doi.org/10.13026/h995-bt35
Harvard
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., Amraei, D., Anibal, J., Aradi, S., Armosh, K., Martinez, A. S., Awan, S., Bedrick, S., Beltran, H., Bernier, A., Berrios, M., Bevers, I., Blatter, A., Brito, R., Brown, A., Brown, J., Cadillac, L., Casalino, S., Costello, J., Dalal, A., De Santiago, I., Diaz-Ocampo, E., Doherty-Kirby, A., Ebraheem, M., Eiseman, E., Elmahdy, M., English, R., Evangelista, E., Fletcher, K., Gallois, H., Garrett, G., Gelbard, A., Ghaffar, O., Goldenberg, A., Hanna, K., Hersh, W., Jain, J., Jayachandran, L., Jenney, K., Jenkins, K., Jo, S., Johnson, A., Kalia, A., Kalia, M., Khawa, Z., Kobayashi, K., Kostelnik, C., Krause, A., Krussel, A., Lapadula, E., Leo, G., Levinsky, J., Loewith, C., Mahajan, R., Maharaj, V., Miao, S., Michaels, L., Mifsud, M., Mikhael, M., Moothedan, E., Nafii, Y., Neal, T., Newberry, K., Ng, E., Nickel, C., Peltier, A., Pharr, T., Pnacekova, M., Pontell, M., Potter, J., Premi-Bortolotto, C., Rafatjou, P., Rahman, J., Rajkumar, G., Ramos, J., de Riesthal, M., Rohde, S., Rossi, J., Russell, L., Salvi Cruz, S., Samuel, J., Shah, S., Shawkat, A., Silberholz, E., Stark, J., Su, L., Sudhakar, S. G., Sutherland, D., Swarna Mukhi, V., Tang, J., Taylor, L., Toghranegar, J., Tu, J., Urbano, M., Victor, G., Vinson, K., Wilke, J., Wilson, C., Zanin, M., Zeng, X., Zesiewicz, T., Zhao, R., Zisimopoulos, P., and Ghosh, S. (2026) 'Bridge2AI-Voice Pediatric Dataset' (version 1.1.0),
PhysioNet
. RRID:SCR_007345. Available at:
https://doi.org/10.13026/h995-bt35
Vancouver
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, Amraei D, Anibal J, Aradi S, Armosh K, Martinez A S, Awan S, Bedrick S, Beltran H, Bernier A, Berrios M, Bevers I, Blatter A, Brito R, Brown A, Brown J, Cadillac L, Casalino S, Costello J, Dalal A, De Santiago I, Diaz-Ocampo E, Doherty-Kirby A, Ebraheem M, Eiseman E, Elmahdy M, English R, Evangelista E, Fletcher K, Gallois H, Garrett G, Gelbard A, Ghaffar O, Goldenberg A, Hanna K, Hersh W, Jain J, Jayachandran L, Jenney K, Jenkins K, Jo S, Johnson A, Kalia A, Kalia M, Khawa Z, Kobayashi K, Kostelnik C, Krause A, Krussel A, Lapadula E, Leo G, Levinsky J, Loewith C, Mahajan R, Maharaj V, Miao S, Michaels L, Mifsud M, Mikhael M, Moothedan E, Nafii Y, Neal T, Newberry K, Ng E, Nickel C, Peltier A, Pharr T, Pnacekova M, Pontell M, Potter J, Premi-Bortolotto C, Rafatjou P, Rahman J, Rajkumar G, Ramos J, de Riesthal M, Rohde S, Rossi J, Russell L, Salvi Cruz S, Samuel J, Shah S, Shawkat A, Silberholz E, Stark J, Su L, Sudhakar S G, Sutherland D, Swarna Mukhi V, Tang J, Taylor L, Toghranegar J, Tu J, Urbano M, Victor G, Vinson K, Wilke J, Wilson C, Zanin M, Zeng X, Zesiewicz T, Zhao R, Zisimopoulos P, Ghosh S. Bridge2AI-Voice Pediatric Dataset (version 1.1.0). PhysioNet. 2026. RRID:SCR_007345. Available from:
https://doi.org/10.13026/h995-bt35
BibTeX
Copy
@article{PhysioNet-b2ai-voice-pediatric-1.1.0,
author = {Bensoussan, Yael and Sigaras, Alexandros and Rameau, Anais and Elemento, Olivier and Powell, Maria and Dorr, David and Payne, Philip and Ravitsky, Vardit and Bélisle-Pipon, Jean-Christophe and Bahr, Ruth and Watts, Stephanie and Bolser, Donald and Siu, Jennifer and Lerner-Ellis, Jordan and Rudzicz, Frank and Boyer, Micah and Abdel-Aty, Yassmeen and {Ahmed Syed}, Toufeeq and Amraei, Dona and Anibal, James and Aradi, Stephen and Armosh, Kirollos and Martinez, Ana Sophia and Awan, Shaheen and Bedrick, Steven and Beltran, Helena and Bernier, Alexander and Berrios, Moroni and Bevers, Isaac and Blatter, Alden and Brito, Rahul and Brown, Amy and Brown, Johnathan and Cadillac, Léo and Casalino, Selina and Costello, John and Dalal, Abhijeet and {De Santiago}, Iris and Diaz-Ocampo, Enrique and Doherty-Kirby, Amanda and Ebraheem, Mohamed and Eiseman, Ellie and Elmahdy, Mahmoud and English, Renee and Evangelista, Emily and Fletcher, Kenneth and Gallois, Hortense and Garrett, Gaelyn and Gelbard, Alexander and Ghaffar, Omar and Goldenberg, Anna and Hanna, Karim and Hersh, William and Jain, Jennifer and Jayachandran, Lochana and Jenney, Kaley and Jenkins, Kathy and Jo, Stacy and Johnson, Alistair and Kalia, Ayush and Kalia, Megha and Khawa, Zoha and Kobayashi, Kenji and Kostelnik, Cindy and Krause, Alisa and Krussel, Andrea and Lapadula, Elisa and Leo, Genelle and Levinsky, Justin and Loewith, Chloe and Mahajan, Radhika and Maharaj, Vrishni and Miao, Siyu and Michaels, LeAnn and Mifsud, Matthew and Mikhael, Marian and Moothedan, Elijah and Nafii, Yosef and Neal, Tempestt and Newberry, Karlee and Ng, Evan and Nickel, Christopher and Peltier, Amanda and Pharr, Trevor and Pnacekova, Michaela and Pontell, Matthew and Potter, Jaiden and Premi-Bortolotto, Claire and Rafatjou, Parnaz and Rahman, JM and Rajkumar, Gayathiri and Ramos, John and {de Riesthal}, Michael and Rohde, Sarah and Rossi, Jillian and Russell, Laurie and {Salvi Cruz}, Samantha and Samuel, Joyce and Shah, Suketu and Shawkat, Ahmed and Silberholz, Elizabeth and Stark, John and Su, Lala and Sudhakar, Shrramana Ganesh and Sutherland, Duncan and {Swarna Mukhi}, Venkata and Tang, Jeffrey and Taylor, Luka and Toghranegar, Jamie and Tu, Julie and Urbano, Megan and Victor, Gavin and Vinson, Kimberly and Wilke, Jordan and Wilson, Claire and Zanin, Madeleine and Zeng, Xijie and Zesiewicz, Theresa and Zhao, Robin and Zisimopoulos, Pantelis and Ghosh, Satrajit},
title = {{Bridge2AI-Voice Pediatric Dataset}},
journal = {{PhysioNet}},
year = {2026},
month = may,
note = {Version 1.1.0},
doi = {10.13026/h995-bt35},
url = {https://doi.org/10.13026/h995-bt35}
}
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Pollard, T., Moody, B. E., Lehman, L., Gow, B., Fernandes, C., Xie, C., Johnson, A., Mark, R. G., & Heldt, T. (2026). PhysioNet as a global platform for biomedical research. Nature Health. https://doi.org/10.1038/s44360-026-00096-z. Available from: https://rdcu.be/faatM
Cite
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APA
Pollard, T., Moody, B. E., Lehman, L., Gow, B., Fernandes, C., Xie, C., Johnson, A., Mark, R. G., & Heldt, T. (2026). PhysioNet as a global platform for biomedical research. Nature Health. https://doi.org/10.1038/s44360-026-00096-z. Available from: https://rdcu.be/faatM
MLA
Pollard, Tom, et al. “PhysioNet as a Global Platform for Biomedical Research.” Nature Health, 2026, https://doi.org/10.1038/s44360-026-00096-z. Available from: https://rdcu.be/faatM
CHICAGO
Pollard, Tom, Benjamin E. Moody, Li-wei Lehman, Brian Gow, Chrystinne Fernandes, Chen Xie, Alistair Johnson, Roger G. Mark, and Thomas Heldt. “PhysioNet as a Global Platform for Biomedical Research.” Nature Health (2026). https://doi.org/10.1038/s44360-026-00096-z.i Available from: https://rdcu.be/faatM
HARVARD
Pollard, T., Moody, B.E., Lehman, L., Gow, B., Fernandes, C., Xie, C., Johnson, A., Mark, R.G. and Heldt, T., 2026. PhysioNet as a global platform for biomedical research. Nature Health. Available at: https://doi.org/10.1038/s44360-026-00096-z. Available from: https://rdcu.be/faatM
VANCOUVER
Pollard T, Moody BE, Lehman L, Gow B, Fernandes C, Xie C, et al. PhysioNet as a global platform for biomedical research. Nature Health. 2026. doi:10.1038/s44360-026-00096-z. Available from: https://rdcu.be/faatM
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Abstract
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 Pediatric Dataset v1.1.0 contains derived audio features for 23,533 recordings collected from 300 participants aged 2-18. 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.
Background
Understanding voice and speech development in children is essential for identifying communication or speech disorders early in life, and for supporting timely intervention [1]. Pediatric and adult voice/speech production are fundamentally different because the respiratory system and larynx undergo rapid functional maturation/development throughout childhood [2, 3]. These developmental changes can influence acoustic features such as fundamental frequency (F₀) [2]. As a result, evidence/normative data that we have in adults cannot be generalized to pediatric populations.
Despite the clinical importance of detecting pediatric communication disorders such as autism spectrum disorder and speech delays, the availability of large-scale pediatric databases/datasets remains/is limited. Data collection in pediatric populations introduces/poses unique challenges such as privacy issues, consent processes, the need for developmentally appropriate tasks. These factors have contributed to the lack of publicly/open access/ available pediatric data sets that enable machine learning for pediatric voice analysis [3].
Establishing a robust multi-institutional dataset that integrates pediatric voice data with demographic information would advance the understanding of voice and disease as well as early detection and intervention. Resources such as this project are intended to enable study of developmental norms, create AI-driven tools for early screening, and support clinical insight.
Methods
Patients/healthy volunteers at the Hospital for Sick Children were considered for enrollment in the study. Patients were considered eligible for the study if they fulfilled the inclusion criteria of 2 to 18 years of age, and English proficiency. Exclusion criteria included participants over 18 years of age, and individuals who were non-verbal. Non-patients, recruited through research postings, were evaluated for eligibility based on the study’s inclusion and exclusion criteria. Following confirmation of eligibility, parental or participant consent was obtained prior to data collection and data sharing. Once consented, patients were assigned a unique study identification number and a standardized age-appropriate protocol for data collection was adopted. The protocol included the collection of demographic information, voice and speech related questionnaires, and questionnaires inquiring about medical history.
All data was collected through customized software –
reproschema-ui –
on tablets. A headset was used to record for most participants, while the remaining recordings utilized the built-in tablet microphone due to low tolerance of wearing headphones or existing complex medical conditions. All participants completed the recording and demographic data collection in one session. For participants without adequate comprehension and familiarity with their past medical history, parents or decision-makers completed the survey during the recording on a separate tablet. The simultaneous completion of the recording and survey improved efficiency and minimized participant burden and fatigue. Data were exported and converted to tab delimited values using an open source library developed by our team [4].
Data Description
The dataset contains both derived audio data features (under features) and phenotypic information acquired during data collection (under phenotype), as well as metadata information for the recordings (available under metadata). Binary files are made available as Parquet, an open-source column-oriented data file format. Each of the parquet files is formatted similarly. Each element of the parquet formatted dataset contains a unique identifier for the participant (participant_id), a unique identifier for the recording session (session_id), the task performed (task_name), the number of time frames associated with that feature (n_frames), and the tensor data for the feature.
torchaudio_spectrograms.parquet (n=23533) contains spectrograms of dimension 201xT generated using the short-time Fast Fourier Transform (FFT) with a 25ms window size, 10ms hop length, and a 400-point FFT.
torchaudio_mel_spectrograms.parquet (n=23533) contains Mel spectrograms of dimension 60xT generated with a 25ms window size, 10ms hop length, a 400-point FFT and 60 Mel bins.
torchaudio_mfcc.parquet (n=23533) contains Mel-frequency cepstrum coefficients of dimension 60xT using the same parameters as the mel spectrograms.
torchaudio_pitch.parquet (n=23533) contains the detected pitch (fundamental frequency) over time and is of dimension T with a min and max pitch of 80 and 500 respectively.
sparc_ema.parquet (n=23532) contains the estimated electromagnetic articulography (EMA) using a deep learning model with dimensions Tx12 where the 12 correspond to X/Y positions of six articulators: tongue dorsum (TD), tongue body (TB), tongue tip (TT), lower incisor (LI), upper lip (UL), lower lip (LL), respectively.
sparc_loudness.parquet (n=23532) contains the estimated loudness based on the average absolute amplitude of the audio waveform of size T, using 20ms windows.
sparc_periodicity.parquet (n=23532) contains the estimated periodicity (confidence of pitch presence) derived from the audio using 20ms windows of dimension T.
sparc_pitch.parquet (n=23532) contains the estimated fundamental frequency (F0) of the audio signal using a different algorithm than before with a range of 50-550Hz and dimension T.
ppgs.parquet (n=23533) contains the phonetic posteriorgram probabilities across 40 phoneme categories giving a dimension of 40xT with a frame rate of 100Hz.
Spectrograms, Mel Spectrograms, MFC coefficients, PPGs, and EMAs for sensitive records and audio checks have been removed from v1.1. Additionally, some files, whether due to length or other issues, could not generate certain features and so are not included in the bundled data.
In addition to the parquet files, the features folder contains the following plain-text file, features derived from the open-source Speech and Music Interpretation by Large-space Extraction (openSMILE [5]), Praat [6], parselmouth [7], and torchaudio [8, 9] are provided. Each feature is present in the static_features.tsv file. There is also metrics related to audio quality derived from the recordings present in the audio_quality_metrics.tsv file.
All of the above files are associated with a data dictionary file which has the same file stem and a JSON suffixes (e.g. torch_spectrogram.json). The above data dictionaries have the same overall structure: a dictionary where keys are the column names matching the associated data file, and values are dictionaries with further detail. The description value in the data dictionary provides a one sentence summary of the respective column.
The code used to preprocess the raw audio waveforms into the parquet file and to merge the source data into the phenotype files has been made open source in the
b2aiprep library
[4].
Usage Notes
If using Python, the parquet dataset can be loaded in with the HuggingFace datasets library as follows:
from datasets import Dataset
ds = Dataset.from_parquet("torchaudio_spectrogram.parquet")
A spectrogram can be plotted in decibels by converting it from its original power representation:
from datasets import Dataset
import pandas as pd
import matplotlib.pyplot as plt
import librosa
import numpy as np
ds = Dataset.from_parquet("torchaudio_mel_spectrogram.parquet")
spectrogram = librosa.power_to_db(np.asarray(ds[0]['mel_spectrogram']))
plt.figure(figsize=(10, 4))
plt.imshow(spectrogram, aspect='auto', origin='lower')
plt.title('Spectrogram')
plt.xlabel('Time Step')
plt.ylabel('Frequency')
plt.colorbar()
plt.show()
A phenotype file can be loaded with any statistical analysis tool. For example, the pandas library in Python can read the data:
import pandas as pd
df = pd.read_csv("demographics.tsv", sep="\t", header=0)
Release Notes
b2ai-voice-pediatric v1.1: No new participants released in this minor update, but releasing audio features for all free speech tasks that were manually checked for presence of unconsented speakers and PII. Additionally, releases new metrics related to the audio quality of the recordings and per recording metadata information.
b2ai-voice-pediatric v1.0: This was the first release of the Bridge2AI-Voice Pediatric dataset.
Ethics
Data collection and sharing was approved by the Research Ethics Board at the Hospital for Sick Children.
Acknowledgements
This release would not be possible without the graceful contribution of data from all the participants of the study.
This project was funded by NIH project number 3OT2OD032720-01S1: Bridge2AI: Voice as a Biomarker of Health - Building an ethically sourced, bioaccoustic database to understand disease like never before. We would also like to thank the NIH for their continued support of the project.
Conflicts of Interest
None to declare.
References
Kelchner, L. N., Brehm, S. B., de Alarcon, A., & Weinrich, B. (2012). Update on pediatric voice and airway disorders: assessment and care. Current opinion in otolaryngology & head and neck surgery, 20(3), 160–164.
https://doi.org/10.1097/MOO.0b013e3283530ecb
Tavares, E. L., Labio, R. B., & Martins, R. H. (2010). Normative study of vocal acoustic parameters from children from 4 to 12 years of age without vocal symptoms: a pilot study. Brazilian journal of otorhinolaryngology, 76(4), 485–490.
https://doi.org/10.1590/S1808-86942010000400013
Fujiki RB, Venkatraman A, Heller Murray ES. The Pediatric Vocal Mechanism: Structure and Function. J Voice. 2025 Apr 4:S0892-1997(25)00118-3. doi: 10.1016/j.jvoice.2025.03.025. Epub ahead of print. PMID: 40187973; PMCID: PMC12353639.
Johnson, A., Bevers, I., Ng, E., Wilke, J., Brito, R., Bedrick, S., Catania, F. & Ghosh, S. (2025). Bridge2AI Data Processing Library (Version 3.0.0) [Computer software].
https://github.com/sensein/b2aiprep
Florian Eyben, Martin Wöllmer, Björn Schuller: "openSMILE - The Munich Versatile and Fast Open-Source Audio Feature Extractor", Proc. ACM Multimedia (MM), ACM, Florence, Italy, ISBN 978-1-60558-933-6, pp. 1459-1462, 25.-29.10.2010.
Boersma P, Van Heuven V. Speak and unSpeak with PRAAT. Glot International. 2001 Nov;5(9/10):341-7.
Jadoul Y, Thompson B, De Boer B. Introducing parselmouth: A python interface to praat. Journal of Phonetics. 2018 Nov 1;71:1-5.
Hwang, J., Hira, M., Chen, C., Zhang, X., Ni, Z., Sun, G., Ma, P., Huang, R., Pratap, V., Zhang, Y., Kumar, A., Yu, C.-Y., Zhu, C., Liu, C., Kahn, J., Ravanelli, M., Sun, P., Watanabe, S., Shi, Y., Tao, T., Scheibler, R., Cornell, S., Kim, S., & Petridis, S. (2023). TorchAudio 2.1: Advancing speech recognition, self-supervised learning, and audio processing components for PyTorch. arXiv preprint arXiv:2310.17864
Yang, Y.-Y., Hira, M., Ni, Z., Chourdia, A., Astafurov, A., Chen, C., Yeh, C.-F., Puhrsch, C., Pollack, D., Genzel, D., Greenberg, D., Yang, E. Z., Lian, J., Mahadeokar, J., Hwang, J., Chen, J., Goldsborough, P., Roy, P., Narenthiran, S., Watanabe, S., Chintala, S., Quenneville-Bélair, V, & Shi, Y. (2021). TorchAudio: Building Blocks for Audio and Speech Processing. arXiv preprint arXiv:2110.15018.
Contents
Abstract
Background
Methods
Data Description
Usage Notes
Release Notes
Ethics
Acknowledgements
Conflicts of Interest
References
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DOI (version 1.1.0):
https://doi.org/10.13026/h995-bt35
DOI (latest version):
https://doi.org/10.13026/mf9s-5r03
Topics:
health
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biomarkers
bridge2ai
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Supported by the National Institute of Biomedical Imaging and Bioengineering (NIBIB), National Heart Lung and Blood Institute (NHLBI), and NIH Office of the Director under NIH grant numbers U24EB037545 and R01EB030362
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