Matching Items (2)
Description
The purpose of the present study was to determine if an automated speech perception task yields results that are equivalent to a word recognition test used in audiometric evaluations. This was done by testing 51 normally hearing adults using a traditional word recognition task (NU-6) and an automated Non-Word Detection task. Stimuli for each task were presented in quiet as well as in six signal-to-noise ratios (SNRs) increasing in 3 dB increments (+0 dB, +3 dB, +6 dB, +9 dB, + 12 dB, +15 dB). A two one-sided test procedure (TOST) was used to determine equivalency of the two tests. This approach required the performance for both tasks to be arcsine transformed and converted to z-scores in order to calculate the difference in scores across listening conditions. These values were then compared to a predetermined criterion to establish if equivalency exists. It was expected that the TOST procedure would reveal equivalency between the traditional word recognition task and the automated Non-Word Detection Task. The results confirmed that the two tasks differed by no more than 2 test items in any of the listening conditions. Overall, the results indicate that the automated Non-Word Detection task could be used in addition to, or in place of, traditional word recognition tests. In addition, the features of an automated test such as the Non-Word Detection task offer additional benefits including rapid administration, accurate scoring, and supplemental performance data (e.g., error analyses) beyond those obtained in traditional speech perception measures.
ContributorsStahl, Amy Nicole (Author) / Pittman, Andrea (Thesis director) / Boothroyd, Arthur (Committee member) / McBride, Ingrid (Committee member) / School of Human Evolution and Social Change (Contributor) / Department of Speech and Hearing Science (Contributor) / Barrett, The Honors College (Contributor)
Created2017-05
Description
Applications over a gesture-based human-computer interface (HCI) require a new user login method with gestures because it does not have traditional input devices. For example, a user may be asked to verify the identity to unlock a device in a mobile or wearable platform, or sign in to a virtual site over a Virtual Reality (VR) or Augmented Reality (AR) headset, where no physical keyboard or touchscreen is available. This dissertation presents a unified user login framework and an identity input method using 3D In-Air-Handwriting (IAHW), where a user can log in to a virtual site by writing a passcode in the air very fast like a signature. The presented research contains multiple tasks that span motion signal modeling, user authentication, user identification, template protection, and a thorough evaluation in both security and usability. The results of this research show around 0.1% to 3% Equal Error Rate (EER) in user authentication in different conditions as well as 93% accuracy in user identification, on a dataset with over 100 users and two types of gesture input devices. Besides, current research in this area is severely limited by the availability of the gesture input device, datasets, and software tools. This study provides an infrastructure for IAHW research with an open-source library and open datasets of more than 100K IAHW hand movement signals. Additionally, the proposed user identity input method can be extended to a general word input method for both English and Chinese using limited training data. Hence, this dissertation can help the research community in both cybersecurity and HCI to explore IAHW as a new direction, and potentially pave the way to practical adoption of such technologies in the future.
ContributorsLu, Duo (Author) / Huang, Dijiang (Thesis advisor) / Li, Baoxin (Committee member) / Zhang, Junshan (Committee member) / Yang, Yezhou (Committee member) / Arizona State University (Publisher)
Created2021