3 citations · 5 across the 4 of their papers we have counts for
5 papers
Structured State Space Decoder for Speech Recognition and Synthesis
Koichi Miyazaki, Masato Murata, Tomoki Koriyama
Automatic speech recognition (ASR) systems developed in recent years have shown promising results with self-attention models (e.g., Transformer and Conformer), which are replacing…
Multi-speaker Text-to-speech Synthesis Using Deep Gaussian Processes
Kentaro Mitsui, Tomoki Koriyama, Hiroshi Saruwatari
Multi-speaker speech synthesis is a technique for modeling multiple speakers' voices with a single model. Although many approaches using deep neural networks (DNNs) have been propo…
Utterance-level Sequential Modeling For Deep Gaussian Process Based Speech Synthesis Using Simple Recurrent Unit
Tomoki Koriyama, Hiroshi Saruwatari
This paper presents a deep Gaussian process (DGP) model with a recurrent architecture for speech sequence modeling. DGP is a Bayesian deep model that can be trained effectively wit…
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…
Generative Moment Matching Network-based Random Modulation Post-filter for DNN-based Singing Voice Synthesis and Neural Double-tracking
Hiroki Tamaru, Yuki Saito, Shinnosuke Takamichi +2
This paper proposes a generative moment matching network (GMMN)-based post-filter that provides inter-utterance pitch variation for deep neural network (DNN)-based singing voice sy…