ON USING NON-LINEAR CANONICAL CORRELATION ANALYSIS FOR VOICE CONVERSION BASED ON GAUSSIAN MIXTURE MODEL

Voice conversion algorithm aims to provide high level of similarity to the target voice with an acceptable level of quality.The main object of this paper was to build a nonlinear relationship between the parameters for the acoustical features of source and target speaker using Non-Linear Canonical Correlation Analysis(NLCCA) based on jointed Gaussian mixture model.Speaker indi-viduality transformation was achieved mainly by altering vocal tract characteristics represented by Line Spectral Frequencies(LSF).To obtain the transformed speech which sounded more like the target voices,prosody modification is involved through residual prediction.Both objective and subjective evaluations were conducted.The experimental results demonstrated that our proposed algorithm was effective and outperformed the conventional conversion method utilized by the Minimum Mean Square Error(MMSE) estimation.

Use of a Neural Network to Measure the Impact of Social Distribution and Access to Infrastructure on the HDI of the Municipalities of Mexico

The Human Development Index (HDI) was created by the United Nations (UN) and is the basis for many other indicators, as well as being the origin of many public policies worldwide. It is a summary measure of life expectancy, education, and per capita income. These components, in addition to being global measures, show difficulty in being impacted and, with this, advancing in the level of human development. This work shows a model that relates variables of social distribution and access to infrastructure in Mexico, with the HDI. These variables were chosen through a statistical analysis based on a set of indicators measured by the National Institute of Statistics and Geography (INEGI) periodically at the municipal level. The statistical analysis shows that there is no simple correlation between these variables and the HDI, so that a supervised learning model based on a neural network was used, therefore proposing a classification technique based on the distribution of data in the underlying metric space. In addition, an attempt was made to find the simplest possible model to reduce the computational cost and in turn obtain information on the variables with the greatest impact on the HDI, with the aim of facilitating the creation of public policies that impact it.

The Research of Fractal Characteristics of the Electrocardiogram in a Real Time Mode

The article presents the results of recent investigations into Holter monitoring of ECG, using non-linear analysis methods. This paper discusses one of the modern methods of time series analysis--a method of deterministic chaos theory. It involves the transition from study of the characteristics of the signal to the investigation of metric （and probabilistic） properties of the reconstructed attractor of the signal. It is shown that one of the most precise characteristics of the functional state of biological systems is the dynamical trend of correlation dimension and entropy of the reconstructed attractor. On the basis of this it is suggested that a complex programming apparatus be created for calculating these characteristics on line. A similar programming product is being created now with the support of RFBR. The first results of the working program, its adjustment, and further development, are also considered in the article.