88 citations · 286 across the 82 of their papers we have counts for
9 papers · 1 filter
Drum-to-Vocal Percussion Sound Conversion and Its Evaluation Methodology
Rinka Nobukawa, Makito Kitamura, Tomohiko Nakamura +2
This paper defines the novel task of drum-to-vocal percussion (VP) sound conversion. VP imitates percussion instruments through human vocalization and is frequently employed in con…
Spatial-CLAP: Learning Spatially-Aware audio--text Embeddings for Multi-Source Conditions
Kentaro Seki, Yuki Okamoto, Kouei Yamaoka +3
Contrastive language--audio pretraining (CLAP) has achieved remarkable success as an audio--text embedding framework, but existing approaches are limited to monaural or single-sour…
Analysing the Language of Neural Audio Codecs
Joonyong Park, Shinnosuke Takamichi, David M. Chan +3
This study presents a comparative analysis of the statistical and linguistic properties of neural audio codecs (NACs). We investigate discrete speech tokens produced by various NAC…
Active Learning for Text-to-Speech Synthesis with Informative Sample Collection
Kentaro Seki, Shinnosuke Takamichi, Takaaki Saeki +1
The construction of high-quality datasets is a cornerstone of modern text-to-speech (TTS) systems. However, the increasing scale of available data poses significant challenges, inc…
Voice Conversion for Likability Control via Automated Rating of Speech Synthesis Corpora
Hitoshi Suda, Shinnosuke Takamichi, Satoru Fukayama
Perceived voice likability plays a crucial role in various social interactions, such as partner selection and advertising. A system that provides reference likable voice samples ta…
AudioBERTScore: Objective Evaluation of Environmental Sound Synthesis Based on Similarity of Audio embedding Sequences
Minoru Kishi, Ryosuke Sakai, Shinnosuke Takamichi +2
We propose a novel objective evaluation metric for synthesized audio in text-to-audio (TTA), aiming to improve the performance of TTA models. In TTA, subjective evaluation of the s…
RELATE: Subjective evaluation dataset for automatic evaluation of relevance between text and audio
Yusuke Kanamori, Yuki Okamoto, Taisei Takano +3
In text-to-audio (TTA) research, the relevance between input text and output audio is an important evaluation aspect. Traditionally, it has been evaluated from both subjective and…
TTSOps: A Closed-Loop Corpus Optimization Framework for Training Multi-Speaker TTS Models from Dark Data
Kentaro Seki, Shinnosuke Takamichi, Takaaki Saeki +1
This paper presents TTSOps, a fully automated closed-loop framework for constructing multi-speaker text-to-speech (TTS) systems from noisy, uncurated web-scale speech data, often r…
Exploring the Effect of Segmentation and Vocabulary Size on Speech Tokenization for Speech Language Models
Shunsuke Kando, Yusuke Miyao, Shinnosuke Takamichi
The purpose of speech tokenization is to transform a speech signal into a sequence of discrete representations, serving as the foundation for speech language models (SLMs). While s…