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NoteWordy: Investigating Touch and Speech Input on Smartphones for Personal Data Capture

Yuhan Luo,Bongshin Lee,Young-Ho Kim,E. Choe

Published 2022 in Proc. ACM Hum. Comput. Interact.

ABSTRACT

Speech as a natural and low-burden input modality has great potential to support personal data capture. However, little is known about how people use speech input, together with traditional touch input, to capture different types of data in self-tracking contexts. In this work, we designed and developed NoteWordy, a multimodal self-tracking application integrating touch and speech input, and deployed it in the context of productivity tracking for two weeks (N = 17). Our participants used the two input modalities differently, depending on the data type as well as personal preferences, error tolerance for speech recognition issues, and social surroundings. Additionally, we found speech input reduced participants' diary entry time and enhanced the data richness of the free-form text. Drawing from the findings, we discuss opportunities for supporting efficient personal data capture with multimodal input and implications for improving the user experience with natural language input to capture various self-tracking data.

PUBLICATION RECORD

  • Publication year

    2022

  • Venue

    Proc. ACM Hum. Comput. Interact.

  • Publication date

    2022-11-14

  • Fields of study

    Computer Science

  • Identifiers
    DOI 10.1145/3567734
  • External record

    Open on Semantic Scholar

  • Source metadata

    Semantic Scholar

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