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Institution

Nagoya Institute of Technology

EducationNagoya, Japan
About: Nagoya Institute of Technology is a education organization based out in Nagoya, Japan. It is known for research contribution in the topics: Thin film & Turbulence. The organization has 10766 authors who have published 19140 publications receiving 255696 citations. The organization is also known as: Nagoya Kōgyō Daigaku & Nitech.


Papers
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Journal ArticleDOI
TL;DR: In this article, a morphotropic phase boundary between orthorhombic and tetragonal phases is found in the composition range 0.05
Abstract: Lead-free piezoelectric ceramics (1−x)(Na0.5K0.5)NbO3–xLiNbO3 {[Lix(Na0.5K0.5)1−x]NbO3} (x=0.04–0.20) have been synthesized by an ordinary sintering technique. The materials with perovskite structure is orthorhombic phase at x⩽0.05 and becomes tetragonal phase at x⩾0.07, a phase K3Li2Nb5O15 with tetragonal tungsten bronze structure begins to appear at x=0.08 and becomes dominant with increasing the content of LiNbO3. A morphotropic phase boundary between orthorhombic and tetragonal phases is found in the composition range 0.05

1,354 citations

Journal ArticleDOI
15 Apr 2007
TL;DR: This paper gives a general overview of techniques in statistical parametric speech synthesis, and contrasts these techniques with the more conventional unit selection technology that has dominated speech synthesis over the last ten years.
Abstract: This paper gives a general overview of techniques in statistical parametric speech synthesis. One of the instances of these techniques, called HMM-based generation synthesis (or simply HMM-based synthesis), has recently been shown to be very effective in generating acceptable speech synthesis. This paper also contrasts these techniques with the more conventional unit selection technology that has dominated speech synthesis over the last ten years. Advantages and disadvantages of statistical parametric synthesis are highlighted as well as identifying where we expect the key developments to appear in the immediate future.

1,270 citations

Proceedings ArticleDOI
05 Jun 2000
TL;DR: A speech parameter generation algorithm for HMM-based speech synthesis, in which the speech parameter sequence is generated from HMMs whose observation vector consists of a spectral parameter vector and its dynamic feature vectors, is derived.
Abstract: This paper derives a speech parameter generation algorithm for HMM-based speech synthesis, in which the speech parameter sequence is generated from HMMs whose observation vector consists of a spectral parameter vector and its dynamic feature vectors. In the algorithm, we assume that the state sequence (state and mixture sequence for the multi-mixture case) or a part of the state sequence is unobservable (i.e., hidden or latent). As a result, the algorithm iterates the forward-backward algorithm and the parameter generation algorithm for the case where the state sequence is given. Experimental results show that by using the algorithm, we can reproduce clear formant structure from multi-mixture HMMs as compared with that produced from single-mixture HMMs.

1,071 citations

Journal ArticleDOI
TL;DR: In this article, a Gaussian mixture model (GMM) of the joint probability density of source and target features is employed for performing spectral conversion between speakers, and a conversion method based on the maximum-likelihood estimation of a spectral parameter trajectory is proposed.
Abstract: In this paper, we describe a novel spectral conversion method for voice conversion (VC). A Gaussian mixture model (GMM) of the joint probability density of source and target features is employed for performing spectral conversion between speakers. The conventional method converts spectral parameters frame by frame based on the minimum mean square error. Although it is reasonably effective, the deterioration of speech quality is caused by some problems: 1) appropriate spectral movements are not always caused by the frame-based conversion process, and 2) the converted spectra are excessively smoothed by statistical modeling. In order to address those problems, we propose a conversion method based on the maximum-likelihood estimation of a spectral parameter trajectory. Not only static but also dynamic feature statistics are used for realizing the appropriate converted spectrum sequence. Moreover, the oversmoothing effect is alleviated by considering a global variance feature of the converted spectra. Experimental results indicate that the performance of VC can be dramatically improved by the proposed method in view of both speech quality and conversion accuracy for speaker individuality.

914 citations


Authors

Showing all 10804 results

NameH-indexPapersCitations
Robert A. Harris6326112828
Kenji Nomura6318434599
Kohsuke Mori6135912631
Ahmed Ali6172815197
Alan W. Black6141319215
Takeo Igarashi5843712791
Takayuki Ozawa5839714088
Itsuo Kodama5831611756
Michio Homma5733210084
Masayuki Nogami5748112619
Naoto Oku5635911124
Lingxia Zhang5515810127
Keiichi Tokuda5535414359
Tomoo Mizugaki552669852
Yoshichika Onuki5343211119
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
202316
202272
2021631
2020718
2019701
2018764