88 citations · 286 across the 82 of their papers we have counts for
12 papers · 1 filter
JVNV: A Corpus of Japanese Emotional Speech with Verbal Content and Nonverbal Expressions
Detai Xin, Junfeng Jiang, Shinnosuke Takamichi +3
We present the JVNV, a Japanese emotional speech corpus with verbal content and nonverbal vocalizations whose scripts are generated by a large-scale language model. Existing emotio…
Coco-Nut: Corpus of Japanese Utterance and Voice Characteristics Description for Prompt-based Control
Aya Watanabe, Shinnosuke Takamichi, Yuki Saito +3
In text-to-speech, controlling voice characteristics is important in achieving various-purpose speech synthesis. Considering the success of text-conditioned generation, such as tex…
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…
Diversity-based core-set selection for text-to-speech with linguistic and acoustic features
Kentaro Seki, Shinnosuke Takamichi, Takaaki Saeki +1
This paper proposes a method for extracting a lightweight subset from a text-to-speech (TTS) corpus ensuring synthetic speech quality. In recent years, methods have been proposed f…
HumanDiffusion: diffusion model using perceptual gradients
Yota Ueda, Shinnosuke Takamichi, Yuki Saito +2
We propose {\it HumanDiffusion,} a diffusion model trained from humans' perceptual gradients to learn an acceptable range of data for humans (i.e., human-acceptable distribution).…
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…
Laughter Synthesis using Pseudo Phonetic Tokens with a Large-scale In-the-wild Laughter Corpus
Detai Xin, Shinnosuke Takamichi, Ai Morimatsu +1
We present a large-scale in-the-wild Japanese laughter corpus and a laughter synthesis method. Previous work on laughter synthesis lacks not only data but also proper ways to repre…
ChatGPT-EDSS: Empathetic Dialogue Speech Synthesis Trained from ChatGPT-derived Context Word Embeddings
Yuki Saito, Shinnosuke Takamichi, Eiji Iimori +2
We propose ChatGPT-EDSS, an empathetic dialogue speech synthesis (EDSS) method using ChatGPT for extracting dialogue context. ChatGPT is a chatbot that can deeply understand the co…
CALLS: Japanese Empathetic Dialogue Speech Corpus of Complaint Handling and Attentive Listening in Customer Center
Yuki Saito, Eiji Iimori, Shinnosuke Takamichi +2
We present CALLS, a Japanese speech corpus that considers phone calls in a customer center as a new domain of empathetic spoken dialogue. The existing STUDIES corpus covers only em…
JNV Corpus: A Corpus of Japanese Nonverbal Vocalizations with Diverse Phrases and Emotions
Detai Xin, Shinnosuke Takamichi, Hiroshi Saruwatari
We present JNV (Japanese Nonverbal Vocalizations) corpus, a corpus of Japanese nonverbal vocalizations (NVs) with diverse phrases and emotions. Existing Japanese NV corpora lack ph…
Environmental sound synthesis from vocal imitations and sound event labels
Yuki Okamoto, Keisuke Imoto, Shinnosuke Takamichi +3
One way of expressing an environmental sound is using vocal imitations, which involve the process of replicating or mimicking the rhythm and pitch of sounds by voice. We can effect…
Learning to Speak from Text: Zero-Shot Multilingual Text-to-Speech with Unsupervised Text Pretraining
Takaaki Saeki, Soumi Maiti, Xinjian Li +3
While neural text-to-speech (TTS) has achieved human-like natural synthetic speech, multilingual TTS systems are limited to resource-rich languages due to the need for paired text…