Výsledky vyhledávání - "Jeong-Uk Bang"

Akademický článek

Joint streaming model for backchannel prediction and automatic speech recognition

Autor: Yong-Seok Choi, Jeong-Uk Bang, Seung Hi Kim

Publikováno v: ETRI Journal, Vol 46, Iss 1, Pp 118-126 (2024)

In human conversations, listeners often utilize brief backchannels such as ''uh-huh'' or ''yeah.'' Timely backchannels are crucial to understanding and increasing trust among conversational partners. In human-machine conversation systems, users can e

Externí odkaz: https://doaj.org/article/90f8ce0e2ac849d9a3401af55d162da5

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Akademický článek

Alzheimer’s disease recognition from spontaneous speech using large language models

Autor: Jeong-Uk Bang, Seung-Hoon Han, Byung-Ok Kang

Publikováno v: ETRI Journal, Vol 46, Iss 1, Pp 96-105 (2024)

We propose a method to automatically predict Alzheimer’s disease from speech data using the ChatGPT large language model. Alzheimer’s disease patients often exhibit distinctive characteristics when describing images, such as difficulties in recal

Externí odkaz: https://doaj.org/article/b515b0b84f684de2ada39a7d6506e208

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Akademický článek

English–Korean speech translation corpus (EnKoST-C): Construction procedure and evaluation results

Autor: Jeong-Uk Bang, Joon-Gyu Maeng, Jun Park, Seung Yun, Sang-Hun Kim

Publikováno v: ETRI Journal, Vol 45, Iss 1, Pp 18-27 (2023)

We present an English–Korean speech translation corpus, named EnKoST-C. End-to-end model training for speech translation tasks often suffers from a lack of parallel data, such as speech data in the source language and equivalent text data in the ta

Externí odkaz: https://doaj.org/article/933eb24927594c4eb7d328e86e0ea1bf

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Akademický článek

KsponSpeech: Korean Spontaneous Speech Corpus for Automatic Speech Recognition

Autor: Jeong-Uk Bang, Seung Yun, Seung-Hi Kim, Mu-Yeol Choi, Min-Kyu Lee, Yeo-Jeong Kim, Dong-Hyun Kim, Jun Park, Young-Jik Lee, Sang-Hun Kim

Publikováno v: Applied Sciences, Vol 10, Iss 19, p 6936 (2020)

This paper introduces a large-scale spontaneous speech corpus of Korean, named KsponSpeech. This corpus contains 969 h of general open-domain dialog utterances, spoken by about 2000 native Korean speakers in a clean environment. All data were constru

Externí odkaz: https://doaj.org/article/0c6450445f7947d3a06e291859a80e85

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Akademický článek

Acoustic Data-Driven Subword Units Obtained through Segment Embedding and Clustering for Spontaneous Speech Recognition

Autor: Jeong-Uk Bang, Sang-Hun Kim, Oh-Wook Kwon

Publikováno v: Applied Sciences, Vol 10, Iss 6, p 2079 (2020)

We propose a method to extend a phoneme set by using a large amount of broadcast data to improve the performance of Korean spontaneous speech recognition. In the proposed method, we first extract variable-length phoneme-level segments from broadcast

Externí odkaz: https://doaj.org/article/7079a4b5e0474ba590b33e4d9f6f08ee

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