Standardization of Korean Speech Audiometry
Junghak Lee
Audiol Speech Res. 2016;12(0):S7-S9. Published online 2016 Mar 31 DOI: http://doi.org/10.21848/asr.2016.12.S1.S7
Citations to this article as recorded by 
Development of a Deep Learning Model for Predicting Speech Audiometry Using Pure-Tone Audiometry Data
Jae Sung Shin, Jun Ma, Seong Jun Choi, Sungyeup Kim, Min Hong
Applied Sciences.2024; 14(20): 9379. CrossRef
Effect of speech-stimulus degradation on phoneme-related potential
Min-Jae Jeon, Jihwan Woo, Rüdiger Land
PLOS ONE.2023; 18(6): e0287584. CrossRef
Objective speech intelligibility prediction using a deep learning model with continuous speech-evoked cortical auditory responses
Youngmin Na, Hyosung Joo, Le Thi Trang, Luong Do Anh Quan, Jihwan Woo
Frontiers in Neuroscience.2022;[Epub] CrossRef
Speech-Perceptual Map Using Confusing Matrix and Application of Shepard’s Law
Chanbeom Kwak, Sunghwa You, Woojae Han, Donguk Lee, Sunmi Ma, Sihoon Park, Yeoju Kim
Audiology and Speech Research.2019; 15(2): 78. CrossRef
Cross-modal effects of noise and thermal conditions on indoor environmental perception and speech recognition
Wonyoung Yang, Hyeun Jun Moon
Applied Acoustics.2018; 141: 1. CrossRef
Effects of indoor water sounds on intrusive noise perception and speech recognition in rooms
Wonyoung Yang, Hyeun Jun Moon
Building Services Engineering Research and Technology.2018; 39(6): 637. CrossRef
Factors affecting hearing deterioration in vestibular schwannoma patients treated with gamma knife radiosurgery: the Asan Medical Center experience
Marn Joon Park, Hong Ju Park, Jong Woo Chung, Do Heui Lee, Young Hyun Cho, Young Jun Choi, Joong Ho Ahn
Acta Oto-Laryngologica.2018; 138(2): 96. CrossRef