• Journal of Audiology & Otology
• /
• v.25 no.4
• /
• pp.171-177
• /
• 2021

Background and Objectives: The purpose of the present study was to validate the performance and diagnostic efficacy of the Korean digits-in-noise (K-DIN) test in comparison to the Korean speech perception-in-noise (K-SPIN) test, which is the representative speech-in-noise test in clinical practice. Subjects and Methods: Twenty-seven subjects (15 normal-hearing and 12 hearing-impaired listeners) participated. The recorded Korean 0-9 digits were used to form quasirandom digit triplets; 50 target digit triplets were presented at the most comfortable level of each subject while presenting speech-shaped background noise at various levels of signal-to-noise ratios (-12.5, -10, -5, or +5 dB). Subjects were then instructed to listen to both target and noise masker unilaterally and bilaterally through a headphone. K-SPIN test was also conducted using the same procedure as the K-DIN. After calculating their percent correct responses, K-DIN and K-SPIN results were compared using a Pearson-correlation test. Results: Results showed a statistically significant correlation between K-DIN and K-SPIN in all hearing conditions (left: r=0.814, p<0.001; right: r=0.788, p<0.001; bilateral: r=0.727, p<0.001). Moreover, the K-DIN test achieved better testing efficacy, shorter average listening time (5 min vs. 30 min), and easier performance of task according to participants' qualitative reports than the K-SPIN test. Conclusions: In this study, the Korean version of digit triplet test was validated in both normal-hearing and hearing-impaired listeners. The findings suggest that the K-DIN test can be used as a simple and time-efficient hearing-in-noise test in audiology clinics in Korea.

• Korean Journal of Audiology
• /
• v.25 no.4
• /
• pp.171-177
• /
• 2021

Background and Objectives: The purpose of the present study was to validate the performance and diagnostic efficacy of the Korean digits-in-noise (K-DIN) test in comparison to the Korean speech perception-in-noise (K-SPIN) test, which is the representative speech-in-noise test in clinical practice. Subjects and Methods: Twenty-seven subjects (15 normal-hearing and 12 hearing-impaired listeners) participated. The recorded Korean 0-9 digits were used to form quasirandom digit triplets; 50 target digit triplets were presented at the most comfortable level of each subject while presenting speech-shaped background noise at various levels of signal-to-noise ratios (-12.5, -10, -5, or +5 dB). Subjects were then instructed to listen to both target and noise masker unilaterally and bilaterally through a headphone. K-SPIN test was also conducted using the same procedure as the K-DIN. After calculating their percent correct responses, K-DIN and K-SPIN results were compared using a Pearson-correlation test. Results: Results showed a statistically significant correlation between K-DIN and K-SPIN in all hearing conditions (left: r=0.814, p<0.001; right: r=0.788, p<0.001; bilateral: r=0.727, p<0.001). Moreover, the K-DIN test achieved better testing efficacy, shorter average listening time (5 min vs. 30 min), and easier performance of task according to participants' qualitative reports than the K-SPIN test. Conclusions: In this study, the Korean version of digit triplet test was validated in both normal-hearing and hearing-impaired listeners. The findings suggest that the K-DIN test can be used as a simple and time-efficient hearing-in-noise test in audiology clinics in Korea.

• Journal of the Korea Institute of Information Security & Cryptology
• /
• v.25 no.5
• /
• pp.1011-1019
• /
• 2015

CAPTCHA(Completely Automated Public Turing test to tell Computers and Humans Apart) is a test used in computing to distinguish whether or not the user is computer or human. Many web sites mostly use the character-based CAPTCHA consisting of digits and characters. Recently, with the development of OCR technology, simple character-based CAPTCHA are broken quite easily. As an alternative, many web sites add noise to make it harder for recognition. In this paper, we analyzed the most recent CAPTCHA, which incorporates the addition of the natural images to obfuscate the characters. We proposed an efficient method using support vector machine to separate the characters from the background image and use convolutional neural network to recognize each characters. As a result, 368 out of 1000 CAPTCHAs were correctly identified, it was demonstrated that the current CAPTCHA is not safe.