An electrostatic model based on charge density is proposed as a model for future force fields. The model is composed of a nucleus and a single Slater-type contracted Gaussian multipole charge density on each atom. The Gaussian multipoles are fit to the electrostatic potential (ESP) calculated at the B3LYP/6-31G* and HF/aug-cc-pVTZ levels of theory and tested by comparing electrostatic dimer ene...

This paper presents a review of various speaker verification approaches in realistic world, and explore a combinational approach between Gaussian Mixture Model (GMM) and Support Vector Machine (SVM) as well as Gaussian Mixture Model (GMM) and Universal Background Model (UBM).

The Gaussian mixture models (GMM) has proved to be an effective probabilistic model for speaker verification, and has been widely used in most of state-of-the-art systems. In this paper, we introduce a new method for the task: that using AdaBoost learning based on the GMM. The motivation is the following: While a GMM linearly combines a number of Gaussian models according to a set of mixing wei...

Gaussian mixture model (GMM) has been widely used for data analysis in various domains including text documents, face images and genes. GMM can be viewed as a simple linear superposition of Gaussian components, each of which represents a data cluster. Recent models, namely Laplacian regularized GMM (LapGMM) and locally consistent GMM (LCGMM) have been proposed to preserve the than the original ...

We have previously developed a one-to-many eigenvoice conversion (EVC) system enabling the conversion from a specific source speaker’s voice into an arbitrary target speaker’s voice. In this system, eigenvoice Gaussian mixture model (EV-GMM) is trained in advance with multiple parallel data sets composed of utterance pairs of the source and many pre-stored target speakers. The EV-GMM is effecti...

This paper presents in-car speech recognition using a modelbased Wiener filter (MBW) and multi-condition (MC) training. The MBW is a 2-step denoising algorithm based on both rough and precise estimation of speech signals. Correcting roughly estimated signals with a Gaussian mixture model (GMM) makes it possible to accurately denoise with little computational cost. In an evaluation of in-car spe...

Maximum likelihood (ML) tting of Gaussian mixture models (GMMs) to feature data is most e ciently handled by the EM algorithm [1, 2, 3, 4]. The EM algorithm is directly applicable to multivariate data in which all the features are always present, and there are no missing values. Unfortunately, missing values are common: caused either by random or systematic e ects. This study presents a novel a...

Gaussian mixture modeling with universal background model (GMM-UBM) is a widely used method for speaker identification, where the GMM model is used to characterize a specific speaker’s voice. The estimation of model parameters is generally performed based on the maximum likelihood (ML) or maximum a posteriori (MAP) criteria. In this way, interspeaker information that discriminates between diffe...

Gaussian Mixture Model (GMM) with Fuzzy c-means attempts to classify signals into speech and music. Feature extraction is done before classification. The classification accuracy mainly relays on the strength of the feature extraction techniques. Simple audio features such as Time domain and Frequency domain are adopted. The time domain features are Zero Crossing Rate (ZCR) and Short Time Energy...