4.7 Article

Modeling Real-World Affective and Communicative Nonverbal Vocalizations From Minimally Speaking Individuals

Journal

IEEE TRANSACTIONS ON AFFECTIVE COMPUTING
Volume 13, Issue 4, Pages 2238-2253

Publisher

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TAFFC.2022.3208233

Keywords

Affective computing; affect sensing and analysis; nonverbal speech; speech analysis

Funding

  1. MIT Media Lab Consortium
  2. Deshpande Center Technology to Improve Ability Program
  3. Apple Scholars in AI/ML
  4. NSF Graduate Research Fellowship Program
  5. Hugh Hampton Young Fellowship Program

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Nonverbal vocalizations from non- and minimally speaking individuals convey important communicative and affective information. This study is among the first to examine the communicative and affective information expressed in nonverbal vocalizations by these individuals. The researchers collected labeled vocalizations in real-world settings and were able to classify them by function with high accuracy.
Nonverbal vocalizations from non- and minimally speaking individuals who speak fewer than 20 words (mv* individuals) convey important communicative and affective information. While nonverbal vocalizations that occur amidst typical speech and infant vocalizations have been studied extensively in the literature, there is limited prior work on vocalizations by mv* individuals. Our work is among the first studies of the communicative and affective information expressed in nonverbal vocalizations by mv* children and adults. We collected labeled vocalizations in real-world settings with eight mv* communicators, with communicative and affective labels provided in-the-moment by a close family member. Using evaluation strategies suitable for messy, real-world data, we show that nonverbal vocalizations can be classified by function (with 4- and 5-way classifications) with F1 scores above chance for all participants. We analyze labeling and data collection practices for each participating family, and discuss the classification results in the context of our novel real-world data collection protocol. The presented work includes results from the largest classification experiments with nonverbal vocalizations from mv* communicators to date.

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