5 papers · 1 filter
SaSLaW: Dialogue Speech Corpus with Audio-visual Egocentric Information Toward Environment-adaptive Dialogue Speech Synthesis
Osamu Take, Shinnosuke Takamichi, Kentaro Seki +2
This paper presents SaSLaW, a spontaneous dialogue speech corpus containing synchronous recordings of what speakers speak, listen to, and watch. Humans consider the diverse environ…
J-CHAT: Japanese Large-scale Spoken Dialogue Corpus for Spoken Dialogue Language Modeling
Wataru Nakata, Kentaro Seki, Hitomi Yanaka +3
Spoken dialogue is essential for human-AI interactions, providing expressive capabilities beyond text. Developing effective spoken dialogue systems (SDSs) requires large-scale, hig…
Spatial Voice Conversion: Voice Conversion Preserving Spatial Information and Non-target Signals
Kentaro Seki, Shinnosuke Takamichi, Norihiro Takamune +3
This paper proposes a new task called spatial voice conversion, which aims to convert a target voice while preserving spatial information and non-target signals. Traditional voice…
Noise-Robust Voice Conversion by Conditional Denoising Training Using Latent Variables of Recording Quality and Environment
Takuto Igarashi, Yuki Saito, Kentaro Seki +4
We propose noise-robust voice conversion (VC) which takes into account the recording quality and environment of noisy source speech. Conventional denoising training improves the no…
SRC4VC: Smartphone-Recorded Corpus for Voice Conversion Benchmark
Yuki Saito, Takuto Igarashi, Kentaro Seki +4
We present SRC4VC, a new corpus containing 11 hours of speech recorded on smartphones by 100 Japanese speakers. Although high-quality multi-speaker corpora can advance voice conver…