Current Practices in Voice Data Collection and Limitations to Voice AI Research: A National Survey

Author:

Evangelista Emily1ORCID,Kale Rohan2,McCutcheon Desiree3,Rameau Anais4ORCID,Gelbard Alexander5ORCID,Powell Maria5ORCID,Johns Michael6,Law Anthony7,Song Phillip8ORCID,Naunheim Matthew8ORCID,Watts Stephanie9ORCID,Bryson Paul C.10ORCID,Crowson Matthew G.11ORCID,Pinto Jeremy12,Bensoussan Yael13ORCID,

Affiliation:

1. University of South Florida Morsani College of Medicine Tampa Florida U.S.A.

2. Department of Biology University of South Florida Tampa Florida U.S.A.

3. USF Health, University of South Florida Tampa Florida U.S.A.

4. Department of Otolaryngology Head and Neck Surgery Weill Cornell Medical College Ithaca New York U.S.A.

5. Department of Otolaryngology Head and Neck Surgery Vanderbilt University Medical Center Nashville Tennessee U.S.A.

6. Department of Otolaryngology—Head and Neck Surgery Keck College of Medicine University of Southern California Los Angeles California U.S.A.

7. Department of Otolaryngology Emory University School of Medicine Atlanta Georgia U.S.A.

8. Massachusetts Eye and Ear, Division of Laryngology Otolaryngology–Head and Neck Surgery Harvard Medical School Boston Massachusetts U.S.A.

9. Department of Otolaryngology Head and Neck Surgery at University of South Florida Morsani College of Medicine Tampa Florida U.S.A.

10. Department of Otolaryngology Head and Neck Surgery at Cleveland Clinic Cleveland Ohio U.S.A.

11. Massachusetts Eye and Ear Otolaryngology–Head and Neck Surgery Harvard Medical School Boston Massachusetts U.S.A.

12. Mila Quebec Artificial Intelligence Institute Montreal Quebec Canada

13. Division of Laryngology Department of Otolaryngology Head and Neck Surgery at University of South Florida Morsani College of Medicine Tampa Florida U.S.A.

Abstract

IntroductionAccuracy and validity of voice AI algorithms rely on substantial quality voice data. Although commensurable amounts of voice data are captured daily in voice centers across North America, there is no standardized protocol for acoustic data management, which limits the usability of these datasets for voice artificial intelligence (AI) research.ObjectiveThe aim was to capture current practices of voice data collection, storage, analysis, and perceived limitations to collaborative voice research.MethodsA 30‐question online survey was developed with expert guidance from the voicecollab.ai members, an international collaborative of voice AI researchers. The survey was disseminated via REDCap to an estimated 200 practitioners at North American voice centers. Survey questions assessed respondents' current practices in terms of acoustic data collection, storage, and retrieval as well as limitations to collaborative voice research.ResultsSeventy‐two respondents completed the survey of which 81.7% were laryngologists and 18.3% were speech language pathologists (SLPs). Eighteen percent of respondents reported seeing 40%–60% and 55% reported seeing >60 patients with voice disorders weekly (conservative estimate of over 4000 patients/week). Only 28% of respondents reported utilizing standardized protocols for collection and storage of acoustic data. Although, 87% of respondents conduct voice research, only 38% of respondents report doing so on a multi‐institutional level. Perceived limitations to conducting collaborative voice research include lack of standardized methodology for collection (30%) and lack of human resources to prepare and label voice data adequately (55%).ConclusionTo conduct large‐scale multi‐institutional voice research with AI, there is a pertinent need for standardization of acoustic data management, as well as an infrastructure for secure and efficient data sharing.Level of EvidenceLevel 5 Laryngoscope, 2023

Publisher

Wiley

Subject

Otorhinolaryngology

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