Portrait de Jeremy Pinto

Jeremy Pinto

jeremy.pinto@mila.quebec
Scientifique senior en recherche appliquée, Recherche appliquée en apprentissage automatique
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Biographie

Jeremy est un chercheur appliqué senior à Mila avec une spécialisation dans les applications pragmatiques de l'apprentissage profond aux données du monde réel. Il possède plus de 7 ans d'expérience professionnelle dans la conception, la mise en œuvre et le déploiement de pipelines d'apprentissage profond. Bien que sa principale spécialité soit la vision par ordinateur, il détient aussi d’excellentes compétences en traitement du langage naturel et en architectures multi-modalités.

Publications

Current Practices in Voice Data Collection and Limitations to Voice AI Research: A National Survey.
Emily Evangelista
Rohan Kale
Desiree McCutcheon
Anais Rameau
Alexander Gelbard
Maria Powell
Michael Johns
Anthony Law
Phillip Song
Matthew Naunheim
Stephanie Watts
Paul C. Bryson
Matthew G. Crowson
Jeremy Pinto
Yael Bensoussan
INTRODUCTION Accuracy and validity of voice AI algorithms rely on substantial quality voice data. Although commensurable amounts of voice da… (voir plus)ta 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. OBJECTIVE The aim was to capture current practices of voice data collection, storage, analysis, and perceived limitations to collaborative voice research. METHODS A 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. RESULTS Seventy-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%). CONCLUSION To 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 EVIDENCE Level 5 Laryngoscope, 2023.
2023-12-13
The Laryngoscope (publié)
doi.org
Validation of an AI-assisted Treatment Outcome Measure for Gender-Affirming Voice Care: Comparing AI Accuracy to Listener's Perception of Voice Femininity.
Shane Simon
Einav Silverstein
Lauren Timmons-Sund
Jeremy Pinto
M. Eugenia Castro
Karla O’Dell
Michael M. Johns III
Wendy J. Mack
Yael Bensoussan
2023-12-01
Journal of Voice (publié)
doi.org