Search Results for author: Tomoki Koriyama

Found 5 papers, 0 papers with code

Multi-speaker Text-to-speech Synthesis Using Deep Gaussian Processes

no code implementations7 Aug 2020 Kentaro Mitsui, Tomoki Koriyama, Hiroshi Saruwatari

We propose a framework for multi-speaker speech synthesis using deep Gaussian processes (DGPs); a DGP is a deep architecture of Bayesian kernel regressions and thus robust to overfitting.

Gaussian Processes Latent Variable Models +2

Utterance-level Sequential Modeling For Deep Gaussian Process Based Speech Synthesis Using Simple Recurrent Unit

no code implementations22 Apr 2020 Tomoki Koriyama, Hiroshi Saruwatari

This paper presents a deep Gaussian process (DGP) model with a recurrent architecture for speech sequence modeling.

Speech Synthesis

Generative Moment Matching Network-based Random Modulation Post-filter for DNN-based Singing Voice Synthesis and Neural Double-tracking

no code implementations9 Feb 2019 Hiroki Tamaru, Yuki Saito, Shinnosuke Takamichi, Tomoki Koriyama, Hiroshi Saruwatari

To address this problem, we use a GMMN to model the variation of the modulation spectrum of the pitch contour of natural singing voices and add a randomized inter-utterance variation to the pitch contour generated by conventional DNN-based singing voice synthesis.

Singing Voice Synthesis Speech Quality

Sampling-based speech parameter generation using moment-matching networks

no code implementations12 Apr 2017 Shinnosuke Takamichi, Tomoki Koriyama, Hiroshi Saruwatari

To give synthetic speech natural inter-utterance variation, this paper builds DNN acoustic models that make it possible to randomly sample speech parameters.

Speech Quality Speech Synthesis

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