41 citations · 57 across the 16 of their papers we have counts for
7 papers · 1 filter
Speaker-Conditioned Phrase Break Prediction for Text-to-Speech with Phoneme-Level Pre-trained Language Model
Dong Yang, Yuki Saito, Takaaki Saeki +4
This paper advances phrase break prediction (also known as phrasing) in multi-speaker text-to-speech (TTS) systems. We integrate speaker-specific features by leveraging speaker emb…
Prosody Labeling with Phoneme-BERT and Speech Foundation Models
Tomoki Koriyama
This paper proposes a model for automatic prosodic label annotation, where the predicted labels can be used for training a prosody-controllable text-to-speech model. The proposed m…
VAE-based Phoneme Alignment Using Gradient Annealing and SSL Acoustic Features
Tomoki Koriyama
This paper presents an accurate phoneme alignment model that aims for speech analysis and video content creation. We propose a variational autoencoder (VAE)-based alignment model i…
Frame-Wise Breath Detection with Self-Training: An Exploration of Enhancing Breath Naturalness in Text-to-Speech
Dong Yang, Tomoki Koriyama, Yuki Saito
Developing Text-to-Speech (TTS) systems that can synthesize natural breath is essential for human-like voice agents but requires extensive manual annotation of breath positions in…
Duration-aware pause insertion using pre-trained language model for multi-speaker text-to-speech
Dong Yang, Tomoki Koriyama, Yuki Saito +3
Pause insertion, also known as phrase break prediction and phrasing, is an essential part of TTS systems because proper pauses with natural duration significantly enhance the rhyth…
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…