Výsledky vyhledávání - "Kim, Jae Min"

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A Two-Step Approach for Data-Efficient French Pronunciation Learning

Autor: Lee, Hoyeon, Jang, Hyeeun, Kim, Jong-Hwan, Kim, Jae-Min

Recent studies have addressed intricate phonological phenomena in French, relying on either extensive linguistic knowledge or a significant amount of sentence-level pronunciation data. However, creating such resources is expensive and non-trivial. To

Externí odkaz: http://arxiv.org/abs/2410.05698

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Cross-Lingual Transfer Learning for Phrase Break Prediction with Multilingual Language Model

Autor: Lee, Hoyeon, Yoon, Hyun-Wook, Kim, Jong-Hwan, Kim, Jae-Min

Phrase break prediction is a crucial task for improving the prosody naturalness of a text-to-speech (TTS) system. However, most proposed phrase break prediction models are monolingual, trained exclusively on a large amount of labeled data. In this pa

Externí odkaz: http://arxiv.org/abs/2306.02579

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Akademický článek

Impact of surgery after injury on SDNN and posttraumatic stress disorder development over two years.

Autor: Kim, Jae-Min¹ (AUTHOR) jmkim@chonnam.ac.kr, Kang, Hee-Ju¹ (AUTHOR), Kim, Ju-Wan¹ (AUTHOR), Jang, Hyunseok² (AUTHOR), Kim, Jung-Chul² (AUTHOR), Ju, Jae-Kyun³ (AUTHOR) jkju@chonnam.ac.kr, Lee, Ju-Yeon¹ (AUTHOR), Kim, Sung-Wan¹ (AUTHOR), Shin, Il-Seon¹ (AUTHOR)

Publikováno v: Scientific Reports. 11/14/2024, Vol. 12 Issue 1, p1-9. 9p.

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Period VITS: Variational Inference with Explicit Pitch Modeling for End-to-end Emotional Speech Synthesis

Autor: Shirahata, Yuma, Yamamoto, Ryuichi, Song, Eunwoo, Terashima, Ryo, Kim, Jae-Min, Tachibana, Kentaro

Several fully end-to-end text-to-speech (TTS) models have been proposed that have shown better performance compared to cascade models (i.e., training acoustic and vocoder models separately). However, they often generate unstable pitch contour with au

Externí odkaz: http://arxiv.org/abs/2210.15964

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Language Model-Based Emotion Prediction Methods for Emotional Speech Synthesis Systems

Autor: Yoon, Hyun-Wook, Kwon, Ohsung, Lee, Hoyeon, Yamamoto, Ryuichi, Song, Eunwoo, Kim, Jae-Min, Hwang, Min-Jae

This paper proposes an effective emotional text-to-speech (TTS) system with a pre-trained language model (LM)-based emotion prediction method. Unlike conventional systems that require auxiliary inputs such as manually defined emotion classes, our sys

Externí odkaz: http://arxiv.org/abs/2206.15067

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TTS-by-TTS 2: Data-selective augmentation for neural speech synthesis using ranking support vector machine with variational autoencoder

Autor: Song, Eunwoo, Yamamoto, Ryuichi, Kwon, Ohsung, Song, Chan-Ho, Hwang, Min-Jae, Oh, Suhyeon, Yoon, Hyun-Wook, Kim, Jin-Seob, Kim, Jae-Min

Recent advances in synthetic speech quality have enabled us to train text-to-speech (TTS) systems by using synthetic corpora. However, merely increasing the amount of synthetic data is not always advantageous for improving training efficiency. Our ai

Externí odkaz: http://arxiv.org/abs/2206.14984

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Cross-Speaker Emotion Transfer for Low-Resource Text-to-Speech Using Non-Parallel Voice Conversion with Pitch-Shift Data Augmentation

Autor: Terashima, Ryo, Yamamoto, Ryuichi, Song, Eunwoo, Shirahata, Yuma, Yoon, Hyun-Wook, Kim, Jae-Min, Tachibana, Kentaro

Data augmentation via voice conversion (VC) has been successfully applied to low-resource expressive text-to-speech (TTS) when only neutral data for the target speaker are available. Although the quality of VC is crucial for this approach, it is chal

Externí odkaz: http://arxiv.org/abs/2204.10020

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