HCSDASFeb 22, 2022

Hidden bawls, whispers, and yelps: can text be made to sound more than just its words?

arXiv:2202.10631v1
Originality Incremental advance
AI Analysis

This addresses accessibility and communication issues for caption users by making text convey more than just words, though it is an incremental improvement in multimodal representation.

The paper tackles the problem of lost vocal nuances in text captions by embedding visual representations of prosody into typography, achieving 65% accuracy in matching audio to text in a user study.

Whether a word was bawled, whispered, or yelped, captions will typically represent it in the same way. If they are your only way to access what is being said, subjective nuances expressed in the voice will be lost. Since so much of communication is carried by these nuances, we posit that if captions are to be used as an accurate representation of speech, embedding visual representations of paralinguistic qualities into captions could help readers use them to better understand speech beyond its mere textual content. This paper presents a model for processing vocal prosody (its loudness, pitch, and duration) and mapping it into visual dimensions of typography (respectively, font-weight, baseline shift, and letter-spacing), creating a visual representation of these lost vocal subtleties that can be embedded directly into the typographical form of text. An evaluation was carried out where participants were exposed to this speech-modulated typography and asked to match it to its originating audio, presented between similar alternatives. Participants (n=117) were able to correctly identify the original audios with an average accuracy of 65%, with no significant difference when showing them modulations as animated or static text. Additionally, participants' comments showed their mental models of speech-modulated typography varied widely.

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