EYEditor

Ghosh, Debjyoti; Foong, Pin Sym; Zhao, Shengdong; Liu, Can; Janaka, Nuwan; Erusu, Vinitha

doi:10.1145/3313831.3376173

Cited by 30 publications

(6 citation statements)

References 47 publications

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“…Furthermore, re-speaking was also observed on the Typists' side, but in the context of repeating the Authors' words, as an implicit expression for error correction or prevention. Recent state-of-the-art techniques support re-speaking phrases for eyes-free text editing [12,18] or in multimodal interfaces [17,58]. While these make important steps towards supporting natural dictation, our research reveals there is much more to understand about re-speaking and contributes a detailed understanding of the demands and noises around it.…”

Section: Rq1: How Do Authors Dictate To Typists?mentioning

confidence: 83%

“…For mobile devices, Kumar et al designed Voice typing that uses a marking menu with touch gestures for faster error corrections [27]. Ghosh et al designed EDITalk to support quick identification of sentence boundaries and speech commands [16]. Recently, Ghosh et al also designed a technique called VoiceRev to support 2 common types of eye-free editing: commanding and re-dictation [18].…”

Section: Voice-based Dictation User Interfacesmentioning

confidence: 99%

“…Existing dictation systems focus on transcription and error correction [24,37,51]. There is a lack of support for the entire loop of interaction of text input, including text composition, review and editing [13,15,16]. With existing dictation systems, users start by generating text by speaking, but then typically have to fall back to the keyboard for reviewing and editing it [15].…”

Section: Introductionmentioning

confidence: 99%

“…With existing dictation systems, users start by generating text by speaking, but then typically have to fall back to the keyboard for reviewing and editing it [15]. This switch of modality interrupts the voice-based experience, leading to breakdowns especially in mobile scenarios when people are walking, cooking or driving [16].…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Typist Experiment: an Investigation of Human-to-Human Dictation via Role-play to Inform Voice-based Text Authoring

Liu

Feng

et al. 2022

Proc. ACM Hum.-Comput. Interact.

Self Cite

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Voice dictation is increasingly used for text entry, especially in mobile scenarios. However, the speech-based experience gets disrupted when users must go back to a screen and keyboard to review and edit the text. While existing dictation systems focus on improving transcription and error correction, little is known about how to support speech input for the entire text creation process, including composition, reviewing and editing. We conducted an experiment in which ten pairs of participants took on the roles of authors and typists to work on a text authoring task. By analysing the natural language patterns of both authors and typists, we identified new challenges and opportunities for the design of future dictation interfaces, including the ambiguity of human dictation, the differences between audio-only and with screen, and various passive and active assistance that can potentially be provided by future systems.

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Section: Rq1: How Do Authors Dictate To Typists?mentioning

confidence: 83%

Section: Voice-based Dictation User Interfacesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Typist Experiment: an Investigation of Human-to-Human Dictation via Role-play to Inform Voice-based Text Authoring

Liu

Feng

et al. 2022

Proc. ACM Hum.-Comput. Interact.

Self Cite

View full text Add to dashboard Cite

show abstract

“…One of the difficulties is that AR glasses lack convenient and suitable handwriting input media [41,72]. For most text input methods, it is hard to meet the characteristics of being wearable and flexible required by AR glasses simultaneously [18]. Thus, flexible touch sensors may provide a suitable potential handwriting interface.…”

Section: Handwriting Input Recognitionmentioning

confidence: 99%

Handwriting Velcro

Fang

Zhang

Zhan

et al. 2022

Proc. ACM Interact. Mob. Wearable Ubiquitous Technol.

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Text input is a desired feature for AR glasses. While there already exist various input modalities (e.g., voice, mid-air gesture), the diverse demands required by different input scenarios can hardly be met by the small number of fixed input postures offered by existing solutions. In this paper, we present Handwriting Velcro, a novel text input solution for AR glasses based on flexible touch sensors. The distinct advantage of our system is that it can easily stick to different body parts, thus endowing AR glasses with posture-adaptive handwriting input. We explored the design space of on-body device positions and identified the best interaction positions for various user postures. To flatten users' learning curves, we adapt our device to the established writing habits of different users by training a 36-character (i.e., A-Z, 0-9) recognition neural network in a human-in-the-loop manner. Such a personalization attempt ultimately achieves a low error rate of 0.005 on average for users with different writing styles. Subjective feedback shows that our solution has a good performance in system practicability and social acceptance. Empirically, we conducted a heuristic study to explore and identify the best interaction Position-Posture Correlation. Experimental results show that our Handwriting Velcro excels similar work [6] and commercial product in both practicality (12.3 WPM) and user-friendliness in different contexts.

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An Evaluation of Caret Navigation Methods for Text Editing in Augmented Reality

Dudley

Kristensson

2022

2022 IEEE International Symposium on Mixed and Augmented Reality Adjunct (ISMAR-Adjunct)

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EYEditor

Cited by 30 publications

References 47 publications

Typist Experiment: an Investigation of Human-to-Human Dictation via Role-play to Inform Voice-based Text Authoring

Typist Experiment: an Investigation of Human-to-Human Dictation via Role-play to Inform Voice-based Text Authoring

Handwriting Velcro

An Evaluation of Caret Navigation Methods for Text Editing in Augmented Reality

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