activity
20172024
most citedJSUT corpus: free large-scale Japanese speech corpus for end-to-end speech synthesis

88 citations · 308 across the 88 of their papers we have counts for

collaborators
Showing 2019Show all

7 papers · 1 filter

cs.SD2019

HumanGAN: generative adversarial network with human-based discriminator and its evaluation in speech perception modeling

Kazuki Fujii, Yuki Saito, Shinnosuke Takamichi +2

We propose the HumanGAN, a generative adversarial network (GAN) incorporating human perception as a discriminator. A basic GAN trains a generator to represent a real-data distribut…

cs.SD2019★ 41 cited

JVS corpus: free Japanese multi-speaker voice corpus

Shinnosuke Takamichi, Kentaro Mitsui, Yuki Saito +3

Thanks to improvements in machine learning techniques, including deep learning, speech synthesis is becoming a machine learning task. To accelerate speech synthesis research, we ar…

cs.SD2019

Acceleration of rank-constrained spatial covariance matrix estimation for blind speech extraction

Yuki Kubo, Norihiro Takamune, Daichi Kitamura +1

In this paper, we propose new accelerated update rules for rank-constrained spatial covariance model estimation, which efficiently extracts a directional target source in diffuse b…

cs.SD2019★ 3 cited

V2S attack: building DNN-based voice conversion from automatic speaker verification

Taiki Nakamura, Yuki Saito, Shinnosuke Takamichi +2

This paper presents a new voice impersonation attack using voice conversion (VC). Enrolling personal voices for automatic speaker verification (ASV) offers natural and flexible bio…

eess.AS2019★ 5 cited

DNN-based Speaker Embedding Using Subjective Inter-speaker Similarity for Multi-speaker Modeling in Speech Synthesis

Yuki Saito, Shinnosuke Takamichi, Hiroshi Saruwatari

This paper proposes novel algorithms for speaker embedding using subjective inter-speaker similarity based on deep neural networks (DNNs). Although conventional DNN-based speaker e…

cs.SD2019

Efficient Full-Rank Spatial Covariance Estimation Using Independent Low-Rank Matrix Analysis for Blind Source Separation

Yuki Kubo, Norihiro Takamune, Daichi Kitamura +1

In this paper, we propose a new algorithm that efficiently separates a directional source and diffuse background noise based on independent low-rank matrix analysis (ILRMA). ILRMA…