Voice conversion (VC) is a technique for converting a source speaker’s voice into another speaker’s voice without changing linguistic information. As a typical approach to VC, a statistical method based on Gaussian mixture model (GMM) is used widely. A GMM is trained as a conversion model using a parallel data set composed of many utterance-pairs of source and target speakers. Although this fra...

OBJECTIVE The limitation of small sample size of functional genomics experiments has made it necessary to integrate DNA microarray experimental data from different sources. However, experimentation noises and biases of different microarray platforms have made integrated data analysis challenging. In this work, we propose an integrative computational framework to identify candidate biomarker gen...

We extend the mass-constraint data clustering and vector quantization algorithm to estimate Gaussian Mixture Models (GMMs) as image features applying to the image retrieval problems. The GMM feature is an alternative method to histograms to represent data density distributions. Histograms are well known for their advantages including rotation invariance, low calculation load, and so on. The GMM...

The local robustness properties of generalized method of moments (GMM) estimators and of a broad class of GMM based tests are investigated in a uni"ed framework. GMM statistics are shown to have bounded in#uence if and only if the function de"ning the orthogonality restrictions imposed on the underlying model is bounded. Since in many applications this function is unbounded, it is useful to hav...

In this paper, a new discriminative likelihood score weighting technique is proposed for speaker identification. The proposed method employs a discriminative weighting of frame-level log-likelihood scores with acoustic-phonetic classification in the Gaussian mixture model (GMM)-based speaker identification. Experiments performed on the Aurora noise-corrupted TIMIT database showed that the propo...

Following the very recent line of work on the “generalized min-max” (GMM) kernel [7], this study proposes the “generalized intersection” (GInt) kernel and the related “normalized generalized min-max” (NGMM) kernel. In computer vision, the (histogram) intersection kernel has been popular, and the GInt kernel generalizes it to data which can have both negative and positive entries. Through an ext...

â â â  â â â â â â â â â â â  financial sector is one of the most influential sectors in economic activities. empirical and theoretical studies conducted in recent years have also confirmed the significant role of financial institutions in economic growth. additionally, trade and financial liberalization policies have been particular concerned with strategic policies in developed and developing...

In this paper, we propose a novel frequency-domain approach to double-talk detection (DTD) based on the Gaussian mixture model (GMM). In contrast to a previous approach based on a simple and heuristic decision rule utilizing time-domain crosscorrelations, GMM is applied to a set of feature vectors extracted from the frequency-domain cross-correlation coefficients. Performance of the proposed ap...

A novel approach to the combination of volatility forecasts is discussed. The proposed procedure makes use of the generalized method of moments (GMM) for estimating the combination weights. The asymptotic properties of the GMM estimator are derived while its finite sample properties are assessed by means of a simulation study. The results of an application to a time series of daily returns on t...

Impressive progress has been recently witnessed on deep unsupervised clustering and feature disentanglement. In this paper, we propose a novel method top of one recent architecture with explanation Gaussian mixture model (GMM) membership, accompanied by GMM loss to enhance the clustering. The is optimized explicitly computed parameters under our coupled inspired framework. Specifically, takes a...