88 citations · 127 across the 38 of their papers we have counts for
35 papers · 1 filter
Improving Speech Prosody of Audiobook Text-to-Speech Synthesis with Acoustic and Textual Contexts
Detai Xin, Sharath Adavanne, Federico Ang +3
We present a multi-speaker Japanese audiobook text-to-speech (TTS) system that leverages multimodal context information of preceding acoustic context and bilateral textual context…
Text-to-speech synthesis from dark data with evaluation-in-the-loop data selection
Kentaro Seki, Shinnosuke Takamichi, Takaaki Saeki +1
This paper proposes a method for selecting training data for text-to-speech (TTS) synthesis from dark data. TTS models are typically trained on high-quality speech corpora that cos…
Mid-attribute speaker generation using optimal-transport-based interpolation of Gaussian mixture models
Aya Watanabe, Shinnosuke Takamichi, Yuki Saito +2
In this paper, we propose a method for intermediating multiple speakers' attributes and diversifying their voice characteristics in ``speaker generation,'' an emerging task that ai…
Visual onoma-to-wave: environmental sound synthesis from visual onomatopoeias and sound-source images
Hien Ohnaka, Shinnosuke Takamichi, Keisuke Imoto +3
We propose a method for synthesizing environmental sounds from visually represented onomatopoeias and sound sources. An onomatopoeia is a word that imitates a sound structure, i.e.…
Multi-Task Adversarial Training Algorithm for Multi-Speaker Neural Text-to-Speech
Yusuke Nakai, Yuki Saito, Kenta Udagawa +1
We propose a novel training algorithm for a multi-speaker neural text-to-speech (TTS) model based on multi-task adversarial training. A conventional generative adversarial network…
Speaking-Rate-Controllable HiFi-GAN Using Feature Interpolation
Detai Xin, Shinnosuke Takamichi, Takuma Okamoto +2
This paper presents a speaking-rate-controllable HiFi-GAN neural vocoder. Original HiFi-GAN is a high-fidelity, computationally efficient, and tiny-footprint neural vocoder. We att…