著者

Kei Sawada Kei Hashimoto Keiichiro Oura Yoshihiko Nankaku Keiichi Tokuda

出版者

ACOUSTICAL SOCIETY OF JAPAN

雑誌

Acoustical Science and Technology (ISSN:13463969)

巻号頁・発行日

vol.39, no.2, pp.119-129, 2018-03-01 (Released:2018-03-01)

参考文献数

35

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This paper proposes a method for constructing text-to-speech (TTS) systems for languages with unknown pronunciations. One goal of speech synthesis research is to establish a framework that can be used to construct TTS systems for any written language. Generally, language-specific knowledge is required to construct TTS systems for a new language. However, it is difficult to acquire language-specific knowledge in each new language. Therefore, constructing a TTS system for a new language entails huge costs. To address this problem, we investigate a framework for automatically constructing a TTS system from a target language database consisting of only speech data and corresponding Unicode texts. In the proposed method, pseudo phonetic information of the target language with unknown pronunciation is obtained by a speech recognizer of a rich-resource proxy language. Then, a grapheme-to-phoneme converter and a statistical parametric speech synthesizer are constructed based on the obtained pseudo phonetic information. The proposed method was applied to Japanese and was evaluated in terms of objective and subjective measures. Additionally, we challenged the construction of TTS systems for nine Indian languages using the proposed method, and TTS systems were evaluated in the Blizzard Challenge 2014 and 2015.

著者

Kei SAWADA Akira TAMAMORI Kei HASHIMOTO Yoshihiko NANKAKU Keiichi TOKUDA

出版者

The Institute of Electronics, Information and Communication Engineers

雑誌

IEICE TRANSACTIONS on Information and Systems (ISSN:09168532)

巻号頁・発行日

vol.E99-D, no.12, pp.3119-3131, 2016-12-01

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This paper proposes a Bayesian approach to image recognition based on separable lattice hidden Markov models (SL-HMMs). The geometric variations of the object to be recognized, e.g., size, location, and rotation, are an essential problem in image recognition. SL-HMMs, which have been proposed to reduce the effect of geometric variations, can perform elastic matching both horizontally and vertically. This makes it possible to model not only invariances to the size and location of the object but also nonlinear warping in both dimensions. The maximum likelihood (ML) method has been used in training SL-HMMs. However, in some image recognition tasks, it is difficult to acquire sufficient training data, and the ML method suffers from the over-fitting problem when there is insufficient training data. This study aims to accurately estimate SL-HMMs using the maximum a posteriori (MAP) and variational Bayesian (VB) methods. The MAP and VB methods can utilize prior distributions representing useful prior information, and the VB method is expected to obtain high generalization ability by marginalization of model parameters. Furthermore, to overcome the local maximum problem in the MAP and VB methods, the deterministic annealing expectation maximization algorithm is applied for training SL-HMMs. Face recognition experiments performed on the XM2VTS database indicated that the proposed method offers significantly improved image recognition performance. Additionally, comparative experiment results showed that the proposed method was more robust to geometric variations than convolutional neural networks.

著者

Kazuhiro NAKAMURA Kei HASHIMOTO Yoshihiko NANKAKU Keiichi TOKUDA

出版者

The Institute of Electronics, Information and Communication Engineers

雑誌

IEICE TRANSACTIONS on Information and Systems (ISSN:09168532)

巻号頁・発行日

vol.E97-D, no.6, pp.1438-1448, 2014-06-01

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This paper proposes a novel approach for integrating spectral feature extraction and acoustic modeling in hidden Markov model (HMM) based speech synthesis. The statistical modeling process of speech waveforms is typically divided into two component modules: the frame-by-frame feature extraction module and the acoustic modeling module. In the feature extraction module, the statistical mel-cepstral analysis technique has been used and the objective function is the likelihood of mel-cepstral coefficients for given speech waveforms. In the acoustic modeling module, the objective function is the likelihood of model parameters for given mel-cepstral coefficients. It is important to improve the performance of each component module for achieving higher quality synthesized speech. However, the final objective of speech synthesis systems is to generate natural speech waveforms from given texts, and the improvement of each component module does not always lead to the improvement of the quality of synthesized speech. Therefore, ideally all objective functions should be optimized based on an integrated criterion which well represents subjective speech quality of human perception. In this paper, we propose an approach to model speech waveforms directly and optimize the final objective function. Experimental results show that the proposed method outperformed the conventional methods in objective and subjective measures.

著者

丸山 剛史 橋本 啓 石井 和也 桑島 英理佳 小柏 香穂理 竹井 沙織 Tsuyoshi MARUYAMA Kei HASHIMOTO Kazuya ISHII Erika KUWAJIMA Kahori OGASHIWA Saori TAKEI

出版者

宇都宮大学教育学部

雑誌

宇都宮大学教育学部教育実践紀要 (ISSN:24333506)

巻号頁・発行日

no.5号, pp.323-376, 2018-08

著者

Ryo Nakabayashi Kei Hashimoto Tetsuya Mori Kiminori Toyooka Hiroshi Sudo Kazuki Saito

出版者

Japanese Society for Plant Biotechnology

雑誌

Plant Biotechnology (ISSN:13424580)

巻号頁・発行日

vol.38, no.3, pp.311-315, 2021-09-25 (Released:2021-09-25)

参考文献数

14

被引用文献数

5

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Spatial metabolomics uses imaging mass spectrometry (IMS) to localize metabolites within tissue section. Here, we performed matrix-assisted laser desorption/ionization-Fourier transform ion cyclotron resonance-IMS (MALDI-FTICR-IMS) to identify the localization of asparaptine A, a naturally occurring inhibitor of angiotensin-converting enzyme, in green spears of asparagus (Asparagus officinalis). Spatial metabolome data were acquired in an untargeted manner. Segmentation analysis using the data characterized tissue-type-dependent and independent distribution patterns in cross-sections of asparagus spears. Moreover, asparaptine A accumulated at high levels in developing lateral shoot tissues. Quantification of asparaptine A in lateral shoots using liquid chromatography-tandem mass spectrometry (LC-MS/MS) validated the IMS analysis. These results provide valuable information for understanding the function of asparaptine A in asparagus, and identify the lateral shoot as a potential region of interest for multiomics studies to examine gene-to-metabolite associations in the asparaptine A biosynthesis.