Hisao Kuwabara

Bio: Hisao Kuwabara is an academic researcher from Teikyo University of Science. The author has contributed to research in topics: Speech synthesis & Formant. The author has an hindex of 14, co-authored 26 publications receiving 1428 citations. Previous affiliations of Hisao Kuwabara include University of Tokyo.

Voice conversion through vector quantization

Abstract: The authors propose a new voice conversion technique through vector quantization and spectrum mapping. The basic idea of this technique is to make mapping codebooks which represent the correspondence between different speakers' codebooks. The mapping codebooks for spectrum parameters, power values and pitch frequencies are separately generated using training utterances. This technique makes it possible to precisely control voice individuality. To evaluate the performance of this technique, hearing tests are carried out on two kinds of voice conversions. One is a conversion between male and female speakers, the other is a conversion between male speakers. In the male-to-female conversion experiment, all converted utterances are judged as female, and in the male-to-male conversion, 65% of them are identified as the target speaker. >

Voice conversion through vector quantization

Abstract: A new voice conversion technique through vector quantization and spectrum mapping is proposed. This technique is based on mapping codebooks which represent the cor respondencebetween different speakers' codebooks. The mapping codebooks for spectrum parameters, power values, and pitch frequencies are separately generated using training utterances. This technique makes it possible to precisely control voice individuality. The performance of this technique is confirmed by spectrum distortion and pitch frequency difference. To evaluate the overall performance of this technique, listening tests are carried out on two kinds of voice conversions: one between male and female speakers, the other between male speakers. In the male-to-female conversion experiment, all converted utterances are judged as female, and in the male-to-male conversion, 57% of them are identified as the target speaker.

Continuous probabilistic transform for voice conversion

Abstract: Voice conversion, as considered in this paper, is defined as modifying the speech signal of one speaker (source speaker) so that it sounds as if it had been pronounced by a different speaker (target speaker). Our contribution includes the design of a new methodology for representing the relationship between two sets of spectral envelopes. The proposed method is based on the use of a Gaussian mixture model of the source speaker spectral envelopes. The conversion itself is represented by a continuous parametric function which takes into account the probabilistic classification provided by the mixture model. The parameters of the conversion function are estimated by least squares optimization on the training data. This conversion method is implemented in the context of the HNM (harmonic+noise model) system, which allows high-quality modifications of speech signals. Compared to earlier methods based on vector quantization, the proposed conversion scheme results in a much better match between the converted envelopes and the target envelopes. Evaluation by objective tests and formal listening tests shows that the proposed transform greatly improves the quality and naturalness of the converted speech signals compared with previous proposed conversion methods.

Voice Conversion Based on Maximum-Likelihood Estimation of Spectral Parameter Trajectory

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.

Low-delay voice conversion based on maximum likelihood estimation of spectral parameter trajectory.

Abstract: INTERSPEECH2008: 9th Annual Conference of the International Speech Communication Association, September 22-26, 2008, Brisbane, Australia.

Spectral voice conversion for text-to-speech synthesis

Abstract: A new voice conversion algorithm that modifies a source speaker's speech to sound as if produced by a target speaker is presented. It is applied to a residual-excited LPC text-to-speech diphone synthesizer. Spectral parameters are mapped using a locally linear transformation based on Gaussian mixture models whose parameters are trained by joint density estimation. The LPC residuals are adjusted to match the target speakers average pitch. To study effects of the amount of training on performance, data sets of varying sizes are created by automatically selecting subsets of all available diphones by a vector quantization method. In an objective evaluation, the proposed method is found to perform more reliably for small training sets than a previous approach. In perceptual tests, it was shown that nearly optimal spectral conversion performance was achieved, even with a small amount of training data. However, speech quality improved with increases in the training set size.