Development of a Smartphone-Based Digits-in-Noise Test in Korean: a Hearing Screening Tool for Speech Perception in Noise

Background@#The digits-in-noise (DiN) test is a speech-in-noise test to measure speech recognition threshold in noise adaptively. Herein, we aimed to develop the Korean version of the DiN test to provide a useful hearing screening tool for clinical as well as research purposes.Method: Spoken monosyllabic digits from 0 to 9 were recorded by a female speaker. The test list was constructed such that each digit was placed in three different positions. An optimization procedure was conducted to equate the audibility of each digit. After the optimization, the smartphone application for the Korean DiN (K-DiN) test was developed. For the adaptive measurement procedure, 180 new DiN triplets separated into six lists of 30 were created. Mean speech recognition threshold values for each list and session were measured to examine the test-retest and training effects of the test materials. In addition, speech recognition threshold values measured by different devices were compared to determine whether the speech recognition threshold levels differed. @*Results@#Optimization results showed that the mean speech recognition threshold and slope were −11.55 dB signal-to-noise ratio and 10.21%/dB, respectively, which are comparable to levels shown in different-language versions of the DiN test. The results of the test-retest and training effects revealed no significant differences among the test sessions and lists. Additionally, the mean speech recognition threshold values measured by four different devices were not different, indicating the reliability of the test materials. @*Conclusion@#We believe this study is the first to attempt to develop a K-DiN test. Our results indicate that this test can be used as a potentially reliable hearing screening tool.

Real-Time "Eye-Writing" Recognition Using Electrooculogram.

Eye movements can be used as alternative inputs for human-computer interface (HCI) systems such as virtual or augmented reality systems as well as new communication ways for patients with locked-in syndrome. In this study, we developed a real-time electrooculogram (EOG)-based eye-writing recognition system, with which users can write predefined symbolic patterns with their volitional eye movements. For the "eye-writing" recognition, the proposed system first reconstructs the eye-written traces from EOG waveforms in real-time; then, the system recognizes the intended symbolic inputs with a reliable recognition rate by matching the input traces with the trained eye-written traces of diverse input patterns. Experiments with 20 participants showed an average recognition rate of 87.38% (F1 score) for 29 different symbolic patterns (26 lower case alphabet characters and three functional input patterns representing Space, Backspace, and Enter keys), demonstrating the promise of our EOG-based eye-writing recognition system in practical scenarios.