28 citations · 56 across the 21 of their papers we have counts for
10 papers · 1 filter
SpeechLMScore: Evaluating speech generation using speech language model
Soumi Maiti, Yifan Peng, Takaaki Saeki +1
While human evaluation is the most reliable metric for evaluating speech generation systems, it is generally costly and time-consuming. Previous studies on automatic speech quality…
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…
Virtuoso: Massive Multilingual Speech-Text Joint Semi-Supervised Learning for Text-To-Speech
Takaaki Saeki, Heiga Zen, Zhehuai Chen +6
This paper proposes Virtuoso, a massively multilingual speech-text joint semi-supervised learning framework for text-to-speech synthesis (TTS) models. Existing multilingual TTS typ…
Improving robustness of spontaneous speech synthesis with linguistic speech regularization and pseudo-filled-pause insertion
Yuta Matsunaga, Takaaki Saeki, Shinnosuke Takamichi +1
We present a training method with linguistic speech regularization that improves the robustness of spontaneous speech synthesis methods with filled pause (FP) insertion. Spontaneou…
Empirical Study Incorporating Linguistic Knowledge on Filled Pauses for Personalized Spontaneous Speech Synthesis
Yuta Matsunaga, Takaaki Saeki, Shinnosuke Takamichi +1
We present a comprehensive empirical study for personalized spontaneous speech synthesis on the basis of linguistic knowledge. With the advent of voice cloning for reading-style sp…
Personalized Filled-pause Generation with Group-wise Prediction Models
Yuta Matsunaga, Takaaki Saeki, Shinnosuke Takamichi +1
In this paper, we propose a method to generate personalized filled pauses (FPs) with group-wise prediction models. Compared with fluent text generation, disfluent text generation h…
UTMOS: UTokyo-SaruLab System for VoiceMOS Challenge 2022
Takaaki Saeki, Detai Xin, Wataru Nakata +3
We present the UTokyo-SaruLab mean opinion score (MOS) prediction system submitted to VoiceMOS Challenge 2022. The challenge is to predict the MOS values of speech samples collecte…
vTTS: visual-text to speech
Yoshifumi Nakano, Takaaki Saeki, Shinnosuke Takamichi +2
This paper proposes visual-text to speech (vTTS), a method for synthesizing speech from visual text (i.e., text as an image). Conventional TTS converts phonemes or characters into…
DRSpeech: Degradation-Robust Text-to-Speech Synthesis with Frame-Level and Utterance-Level Acoustic Representation Learning
Takaaki Saeki, Kentaro Tachibana, Ryuichi Yamamoto
Most text-to-speech (TTS) methods use high-quality speech corpora recorded in a well-designed environment, incurring a high cost for data collection. To solve this problem, existin…
SelfRemaster: Self-Supervised Speech Restoration with Analysis-by-Synthesis Approach Using Channel Modeling
Takaaki Saeki, Shinnosuke Takamichi, Tomohiko Nakamura +2
We present a self-supervised speech restoration method without paired speech corpora. Because the previous general speech restoration method uses artificial paired data created by…