Gist and Verbatim: Understanding Speech to Inform New Interfaces for Verbal Text Composition
Brinda Mehra, Kejia Shen, Ryan Yen, Can Liu · 2023
Recent interest in speech-to-text applications has found speech to be an efficient modality for text input. However, the spontaneity of speech makes direct transcriptions of spoken compositions effortful to edit. While previous works in Human-Computer Interaction (HCI) domain focus on improving error correction, there is a lack of theoretical ground around the understanding of speech as an input modality. This work explores literature from Cognitive Science to synthesize relevant theories and findings for the HCI audience to reference. Motivated by the literature indicating a fast memory decay of speech production and a preference towards gist abstraction in memory traces, an experiment was conducted to observe users’ immediate recall of their verbal composition. Based on the theories and findings, we introduce new interaction concepts and workflows that adapt to the characteristics of speech input.