Search Results for author: Daisuke Saito

Found 6 papers, 0 papers with code

Do learned speech symbols follow Zipf's law?

no code implementations18 Sep 2023 Shinnosuke Takamichi, Hiroki Maeda, Joonyong Park, Daisuke Saito, Hiroshi Saruwatari

In this study, we investigate whether speech symbols, learned through deep learning, follow Zipf's law, akin to natural language symbols.

Preliminary Systematic Literature Review of Machine Learning System Development Process

no code implementations12 Oct 2019 Yasuhiro Watanabe, Hironori Washizaki, Kazunori Sakamoto, Daisuke Saito, Kiyoshi Honda, Naohiko Tsuda, Yoshiaki Fukazawa, Nobukazu Yoshioka

Previous machine learning (ML) system development research suggests that emerging software quality attributes are a concern due to the probabilistic behavior of ML systems.

BIG-bench Machine Learning

Wasserstein GAN and Waveform Loss-based Acoustic Model Training for Multi-speaker Text-to-Speech Synthesis Systems Using a WaveNet Vocoder

no code implementations31 Jul 2018 Yi Zhao, Shinji Takaki, Hieu-Thi Luong, Junichi Yamagishi, Daisuke Saito, Nobuaki Minematsu

In order to reduce the mismatched characteristics between natural and generated acoustic features, we propose frameworks that incorporate either a conditional generative adversarial network (GAN) or its variant, Wasserstein GAN with gradient penalty (WGAN-GP), into multi-speaker speech synthesis that uses the WaveNet vocoder.

Generative Adversarial Network Speech Synthesis +1

A Spoofing Benchmark for the 2018 Voice Conversion Challenge: Leveraging from Spoofing Countermeasures for Speech Artifact Assessment

no code implementations23 Apr 2018 Tomi Kinnunen, Jaime Lorenzo-Trueba, Junichi Yamagishi, Tomoki Toda, Daisuke Saito, Fernando Villavicencio, Zhen-Hua Ling

As a supplement to subjective results for the 2018 Voice Conversion Challenge (VCC'18) data, we configure a standard constant-Q cepstral coefficient CM to quantify the extent of processing artifacts.

Benchmarking Speaker Verification +1

The Voice Conversion Challenge 2018: Promoting Development of Parallel and Nonparallel Methods

no code implementations12 Apr 2018 Jaime Lorenzo-Trueba, Junichi Yamagishi, Tomoki Toda, Daisuke Saito, Fernando Villavicencio, Tomi Kinnunen, Zhen-Hua Ling

We present the Voice Conversion Challenge 2018, designed as a follow up to the 2016 edition with the aim of providing a common framework for evaluating and comparing different state-of-the-art voice conversion (VC) systems.

Voice Conversion

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