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Full Description of An Automated Pipeline for Providing Personalized Feedback Based on Audio Samples

Author

Listed:
  • Alejandro Cid

  • Loann Peurey
  • William Havard
  • Gwendal Virlet
  • Xuan-Nga Cao
  • Juanita Bloomfield
  • Ana Balsa
  • Martın Ottavianelli
  • Jose Luis Horta Brasil
  • Alejandrina Cristia

Abstract

Personalized feedback based on the automated analysis of audio samples could be useful in a wide range of intervention contexts, from early childhood to neurodegenerative programs, which target behaviors having vocal correlates. In this paper, we describe an automated pipeline that allows one to provide personalized feedback based on the automated analysis of audio samples of caregiver-child conversations captured using a smartphone. The pipeline relies on open-source packages and AWS in order to provide a cheap, reproducible, and considerably scalable solution for researchers and practitioners interested in early childhood development and caregiver-child interaction, and which could be adapted for other use cases. It processes conversation files that are 1-10 minutes long, with a cost of 0.20 US$ per hour of audio analyzed. It is currently operational in one large-scale experiment in Uruguay, where audio files are collected through a chatbot, whose implementation is not covered in this paper. Finally, we lay out limitations of our approach and potential improvements

Suggested Citation

  • Alejandro Cid & Loann Peurey & William Havard & Gwendal Virlet & Xuan-Nga Cao & Juanita Bloomfield & Ana Balsa & Martın Ottavianelli & Jose Luis Horta Brasil & Alejandrina Cristia, 2024. "Full Description of An Automated Pipeline for Providing Personalized Feedback Based on Audio Samples," Documentos de Trabajo/Working Papers 2409, Facultad de Ciencias Empresariales y Economia. Universidad de Montevideo..
    • Handle: RePEc:mnt:wpaper:2409
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    File URL: https://www2.um.edu.uy/fcee_papers/2020/Full_Description_of_An_Automated_Pipeline_for_Providing_Personalized_Feedback_Based_on_Audio_Samples.pdf
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