1 citations · 1 across the 6 of their papers we have counts for
6 papers
MixedG2P-T5: G2P-free Speech Synthesis for Mixed-script texts using Speech Self-Supervised Learning and Language Model
Joonyong Park, Daisuke Saito, Nobuaki Minematsu
This study presents a novel approach to voice synthesis that can substitute the traditional grapheme-to-phoneme (G2P) conversion by using a deep learning-based model that generates…
EmoSSLSphere: Multilingual Emotional Speech Synthesis with Spherical Vectors and Discrete Speech Tokens
Joonyong Park, Kenichi Nakamura
This paper introduces EmoSSLSphere, a novel framework for multilingual emotional text-to-speech (TTS) synthesis that combines spherical emotion vectors with discrete token features…
Analytic Study of Text-Free Speech Synthesis for Raw Audio using a Self-Supervised Learning Model
Joonyong Park, Daisuke Saito, Nobuaki Minematsu
We examine the text-free speech representations of raw audio obtained from a self-supervised learning (SSL) model by analyzing the synthesized speech using the SSL representations…
A Pilot Study of GSLM-based Simulation of Foreign Accentuation Only Using Native Speech Corpora
Kentaro Onda, Joonyong Park, Nobuaki Minematsu +1
We propose a method of simulating the human process of foreign accentuation using Generative Spoken Language Model (GSLM) only with native speech corpora. When one listens to spoke…
Do learned speech symbols follow Zipf's law?
Shinnosuke Takamichi, Hiroki Maeda, Joonyong Park +2
In this study, we investigate whether speech symbols, learned through deep learning, follow Zipf's law, akin to natural language symbols. Zipf's law is an empirical law that deline…
How Generative Spoken Language Modeling Encodes Noisy Speech: Investigation from Phonetics to Syntactics
Joonyong Park, Shinnosuke Takamichi, Tomohiko Nakamura +3
We examine the speech modeling potential of generative spoken language modeling (GSLM), which involves using learned symbols derived from data rather than phonemes for speech analy…