no code implementations • 10 Apr 2017 • Hiroyuki Miyoshi, Yuki Saito, Shinnosuke Takamichi, Hiroshi Saruwatari
Conventional VC using shared context posterior probabilities predicts target speech parameters from the context posterior probabilities estimated from the source speech parameters.
4 code implementations • 23 Sep 2017 • Yuki Saito, Shinnosuke Takamichi, Hiroshi Saruwatari
In the proposed framework incorporating the GANs, the discriminator is trained to distinguish natural and generated speech parameters, while the acoustic models are trained to minimize the weighted sum of the conventional minimum generation loss and an adversarial loss for deceiving the discriminator.
2 code implementations • 10 Jul 2018 • Shinnosuke Takamichi, Yuki Saito, Norihiro Takamune, Daichi Kitamura, Hiroshi Saruwatari
This paper presents a deep neural network (DNN)-based phase reconstruction from amplitude spectrograms.
Sound Audio and Speech Processing
no code implementations • 9 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.
no code implementations • 19 Jul 2019 • Yuki Saito, Shinnosuke Takamichi, Hiroshi Saruwatari
Although conventional DNN-based speaker embedding such as a $d$-vector can be applied to multi-speaker modeling in speech synthesis, it does not correlate with the subjective inter-speaker similarity and is not necessarily appropriate speaker representation for open speakers whose speech utterances are not included in the training data.
no code implementations • 5 Aug 2019 • Taiki Nakamura, Yuki Saito, Shinnosuke Takamichi, Yusuke Ijima, Hiroshi Saruwatari
The experimental evaluation compares converted voices between the proposed method that does not use the targeted speaker's voice data and the standard VC that uses the data.
Automatic Speech Recognition Automatic Speech Recognition (ASR) +3
no code implementations • 25 Sep 2019 • Kazuki Fujii, Yuki Saito, Shinnosuke Takamichi, Yukino Baba, Hiroshi Saruwatari
To model the human-acceptable distribution, we formulate a backpropagation-based generator training algorithm by regarding human perception as a black-boxed discriminator.
2 code implementations • ECCV 2020 • Yuki Saito, Takuma Nakamura, Hirotaka Hachiya, Kenji Fukumizu
Matching two different sets of items, called heterogeneous set-to-set matching problem, has recently received attention as a promising problem.
no code implementations • LREC 2020 • Yuki Saito, Shinnosuke Takamichi, Hiroshi Saruwatari
Developing a spontaneous speech corpus would be beneficial for spoken language processing and understanding.
no code implementations • LREC 2020 • Yuki Yamashita, Tomoki Koriyama, Yuki Saito, Shinnosuke Takamichi, Yusuke Ijima, Ryo Masumura, Hiroshi Saruwatari
In this paper, we investigate the effectiveness of using rich annotations in deep neural network (DNN)-based statistical speech synthesis.
no code implementations • 8 Feb 2021 • Yota Ueda, Kazuki Fujii, Yuki Saito, Shinnosuke Takamichi, Yukino Baba, Hiroshi Saruwatari
A DNN-based generator is trained using a human-based discriminator, i. e., humans' perceptual evaluations, instead of the GAN's DNN-based discriminator.
2 code implementations • 30 Aug 2021 • Masanari Kimura, Takuma Nakamura, Yuki Saito
This paper addresses the problem of set-to-set matching, which involves matching two different sets of items based on some criteria, especially in the case of high-dimensional items like images.
no code implementations • 28 Mar 2022 • Yuki Saito, Yuto Nishimura, Shinnosuke Takamichi, Kentaro Tachibana, Hiroshi Saruwatari
We describe our methodology to construct an empathetic dialogue speech corpus and report the analysis results of the STUDIES corpus.
no code implementations • 16 Jun 2022 • Yuto Nishimura, Yuki Saito, Shinnosuke Takamichi, Kentaro Tachibana, Hiroshi Saruwatari
To train the empathetic DSS model effectively, we investigate 1) a self-supervised learning model pretrained with large speech corpora, 2) a style-guided training using a prosody embedding of the current utterance to be predicted by the dialogue context embedding, 3) a cross-modal attention to combine text and speech modalities, and 4) a sentence-wise embedding to achieve fine-grained prosody modeling rather than utterance-wise modeling.
no code implementations • 21 Jun 2022 • Kenta Udagawa, Yuki Saito, Hiroshi Saruwatari
With a conventional speaker-adaptation method, a target speaker's embedding vector is extracted from his/her reference speech using a speaker encoder trained on a speaker-discriminative task.
no code implementations • 26 Sep 2022 • Yusuke Nakai, Yuki Saito, Kenta Udagawa, Hiroshi Saruwatari
A conventional generative adversarial network (GAN)-based training algorithm significantly improves the quality of synthetic speech by reducing the statistical difference between natural and synthetic speech.
no code implementations • 27 Feb 2023 • Dong Yang, Tomoki Koriyama, Yuki Saito, Takaaki Saeki, Detai Xin, Hiroshi Saruwatari
We also leverage duration-aware pause insertion for more natural multi-speaker TTS.
no code implementations • 23 May 2023 • Yuki Saito, Shinnosuke Takamichi, Eiji Iimori, Kentaro Tachibana, Hiroshi Saruwatari
We focus on ChatGPT's reading comprehension and introduce it to EDSS, a task of synthesizing speech that can empathize with the interlocutor's emotion.
no code implementations • 23 May 2023 • Yuki Saito, Eiji Iimori, Shinnosuke Takamichi, Kentaro Tachibana, Hiroshi Saruwatari
We present CALLS, a Japanese speech corpus that considers phone calls in a customer center as a new domain of empathetic spoken dialogue.
no code implementations • 19 Jun 2023 • Kenta Oono, Nontawat Charoenphakdee, Kotatsu Bito, Zhengyan Gao, Yoshiaki Ota, Shoichiro Yamaguchi, Yohei Sugawara, Shin-ichi Maeda, Kunihiko Miyoshi, Yuki Saito, Koki Tsuda, Hiroshi Maruyama, Kohei Hayashi
In this paper, we propose Virtual Human Generative Model (VHGM), a machine learning model for estimating attributes about healthcare, lifestyles, and personalities.
no code implementations • 28 Nov 2023 • Takuma Nakamura, Yuki Saito, Ryosuke Goto
In this paper, we formulate the outfit completion problem as a set retrieval task and propose a novel framework for solving this problem.
no code implementations • 28 Nov 2023 • Kazuki Yamauchi, Yusuke Ijima, Yuki Saito
The experimental results demonstrate that our StyleCap leveraging richer LLMs for the text decoder, speech self-supervised learning (SSL) features, and sentence rephrasing augmentation improves the accuracy and diversity of generated speaking-style captions.
no code implementations • 1 Feb 2024 • 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 training data.
no code implementations • 26 Mar 2024 • Masanari Kimura, Ryotaro Shimizu, Yuki Hirakawa, Ryosuke Goto, Yuki Saito
From these observations, we show that Deep Sets, one of the well-known permutation-invariant neural networks, can be generalized in the sense of a quasi-arithmetic mean.