Acceptability of Speech and Silent Speech Input Methods in Private and Public

Pandey, Laxmi; Hasan, Khalad; Arif, Ahmed Sabbir

doi:10.1145/3411764.3445430

Cited by 23 publications

(7 citation statements)

References 100 publications

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“…We invite 15 volunteers (4 females and 11 males) to collect data in an IRB-approved study. Our participants are between the ages of 19-31 years and speak different native languages -English (5), Hindi (4), Telugu (4), Spanish (1), and Kannada (1). 1 Almost all standard dictionaries provide the phonemic map of each word [3].…”

Section: Demonstrationmentioning

confidence: 99%

“…Unvoiced speech provides hands-free intuitive interaction. A recent study [5] shows that users prefer unvoiced speech over acoustic input as an interaction technique, and are willing to tolerate lower performance for maintaining their privacy. On the other hand, the increasing social acceptance of headphones and earphones leads to an alternative ubiquitous sensing modality -ear-worn sensors or earables.…”

Section: Introductionmentioning

confidence: 99%

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Leveraging earables for unvoiced command recognition

Srivastava

Khanna

Pan

et al. 2022

Proceedings of the 20th Annual International Conference on Mobile Systems, Applications and Services

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We demonstrate an ear-worn technology that recognizes unvoiced human commands by tracking jaw motion. The ear-worn system is designed to achieve continual unvoiced command recognition for robust human-computer interaction (HCI) applications. First, the system reliably extracts the jaw motion signals buried under the noise caused by head motion, walking, and other motion artifacts to track single secondary voice articulator (i.e., word). Then, learning from linguistics and human speech anatomy, we design a novel algorithm that localizes the phonemes in the command, and reconstructs the word. We evaluate the proposed system in real-world experiments with 15 volunteers. Our preliminary results show that the proposed system obtains a word recognition accuracy of 95.6% in noise-free conditions and 93.2% and 91.6%, while head nodding and walking. CCS CONCEPTS• Human-centered computing → Accessibility systems and tools; Accessibility;

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Section: Demonstrationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Leveraging earables for unvoiced command recognition

Srivastava

Khanna

Pan

et al. 2022

Proceedings of the 20th Annual International Conference on Mobile Systems, Applications and Services

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“…AR HMDs are meant to be highly portable and enable interaction with multiple users in one physical space. Therefore, as prior research has shown [Alallah et al(2018), Pandey et al(2021), Vergari et al(2021)], the social acceptability of interactions used for AR HMDs can be an important factor in determining their widespread adoption and usability. As such, the interactions should also be socially acceptable and transferable to a range of situations and use cases.…”

Section: Inclusion Criteria For the Techniquesmentioning

confidence: 99%

Exploring Text Selection in Augmented Reality Systems

Liu

Meng

Spittle

et al. 2022

Proceedings of the 18th ACM SIGGRAPH International Conference on Virtual-Reality Continuum and Its Applications in Industry

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Text selection is a common and essential activity during text interaction in all interactive systems. As Augmented Reality (AR) head-mounted displays (HMDs) become more widespread, they will need to provide effective interaction techniques for text selection that ensure users can complete a range of text manipulation tasks (e.g., to highlight, copy, and paste text, send instant messages, and browse the web). As a relatively new platform, text selection in AR is largely unexplored and the suitability of interaction techniques supported by current AR HMDs for text selection tasks is unclear. This research aims to fill this gap and reports on an experiment with 12 participants, which compares the performance and usability (user experience and workload) of four possible techniques (Hand+Pinch, Hand+Dwell, Head+Pinch, and Head+Dwell). Our results suggest that Head+Dwell should be the default selection technique, as it is relatively fast, has the lowest error rate and workload, and has the highestrated user experience and social acceptance.

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“…While HMDs are designed for individual users, their interactions are often quite noticeable. As prior research has shown [1,34,40], the social acceptability of interactions between HMDs and mobile devices can be an important factor in determining their adoption and usability.…”

Section: Usability Criteriamentioning

confidence: 99%

An Exploration of Hands-free Text Selection for Virtual Reality Head-Mounted Displays

Meng

Liang

2022

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

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Hand-based interaction, such as using a handheld controller or making hand gestures, has been widely adopted as the primary method for interacting with both virtual reality (VR) and augmented reality (AR) head-mounted displays (HMDs). In contrast, hands-free interaction avoids the need for users' hands and although it can afford additional benefits, there has been limited research in exploring and evaluating hands-free techniques for these HMDs. As VR HMDs become ubiquitous, people will need to do text editing, which requires selecting text segments. Similar to hands-free interaction, text selection is underexplored. This research focuses on both, text selection via hands-free interaction. Our exploration involves a user study with 24 participants to investigate the performance, user experience, and workload of three hands-free selection mechanisms (Dwell, Blink, Voice) to complement head-based pointing. Results indicate that Blink outperforms Dwell and Voice in completion time and accuracy.Users' subjective feedback also shows that Blink is the preferred technique for text selection. This work is the first to explore hands-free interaction for text selection in VR HMDs and our results provide a solid platform for further research in this important area.

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Acceptability of Speech and Silent Speech Input Methods in Private and Public

Cited by 23 publications

References 100 publications

Leveraging earables for unvoiced command recognition

Leveraging earables for unvoiced command recognition

Exploring Text Selection in Augmented Reality Systems

An Exploration of Hands-free Text Selection for Virtual Reality Head-Mounted Displays

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