• Journal of the Korea Institute of Information and Communication Engineering
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• v.20 no.11
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• pp.2125-2130
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• 2016

In this paper, we researched the excellency of 3D graphic nursery contents which is developed by mobile AR technology. AR technology has currently people's attention because of the potential to be core contents of future ICT industry. We applied AR nursery contents for kid's subtitle language selection in Korean, Chinese and English education. The original fairy tale consisted of 6~8 scenes for the 3D contents production, and was adapted and translated. Dubbing was dubbed by the native speaker using the standard pronunciation, and the effect sound was edited separately to fit the scene. After composing a scenario, constructing a 3D model, constructing a interaction, constructing a sound effect, and creating content metadata, the Unity 3D game engine is executed to create a project and describe it as a script. It provides a fun and informative tradition of fairy tales with abundant content that incorporates ICT technology, accepting advanced technology-based education, and having opportunities to perceive software in daily life.

• Phonetics and Speech Sciences
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• v.12 no.4
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• pp.81-90
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• 2020

This paper describes a method of building Korean conversational speech data in the emergency medical domain and proposes an annotation method for the collected data in order to improve speech recognition performance. To suggest future research directions, baseline speech recognition experiments were conducted by using partial data that were collected and annotated. All voices were recorded at 16-bit resolution at 16 kHz sampling rate. A total of 166 conversations were collected, amounting to 8 hours and 35 minutes. Various information was manually transcribed such as orthography, pronunciation, dialect, noise, and medical information using Praat. Baseline speech recognition experiments were used to depict problems related to speech recognition in the emergency medical domain. The Korean conversational speech data presented in this paper are first-stage data in the emergency medical domain and are expected to be used as training data for developing conversational systems for emergency medical applications.