ABSTRACT: To solve the problems of outliers and high dimensionality of training feature vectors in speaker identification, in this paper, we propose an efficient GMM based on local robust PCA with VQ. The proposed method firstly partitions the data space into several disjoint regions by VQ, and then performs robust PCA using the iteratively reweighted covariance matrix in each region. Finally, ...

This paper presents a new spectral modeling and conversion method for voice conversion. In contrast to the conventional Gaussian mixture model (GMM) based methods, we use restricted Boltzmann machines (RBMs) as probability density models to model the joint distributions of source and target spectral features. The Gaussian distribution in each mixture of GMM is replaced by an RBM, which can bett...

In this paper, a voice activity detection (VAD) method is proposed based on Gaussian mixture models (GMMs) by exploiting the spatial selectivity in dual-microphone environments. In other words, each GMM is constructed according to the direction-ofarrival (DOA) to detect speech intervals. Based on the assumption that the target speech is located in front of dual-microphones, the VAD is performed...

In most state-of-the-art voice conversion systems, speech quality of converted utterances is still unsatisfactory. In this paper, STRAIGHT analysis-synthesis framework is used to improve the quality. A smoothed GMM and MAP adaptation is proposed for spectrum conversion to avoid the overly smooth phenomenon in the traditional GMM method. Since frames are processed independently, the GMM based tr...

In this paper, we present a statistical method based on GMM modeling to map the acoustic speech spectral features to visual features of Cued Speech in the regression criterion of Minimum Mean-Square Error (MMSE) in a low signal level which is innovative and different with the classic text-to-visual approach. Two different training methods for GMM, namely Expecting-Maximization (EM) approach and...

This study focuses on the classification of pathological voice using GMM (Gaussian Mixture Model) and compares the results to the previous work which was done by ANN (Artificial Neural Network). Speech data from normal people and patients were collected, then diagnosed and classified into two different categories. Six characteristic parameters (Jitter, Shimmer, NHR, SPI, APQ and RAP) were chose...

Gaussian mixture models (GMMs) have proven extremely successful for textindependent speaker verification. The standard training method for GMM models is to use MAP adaptation of the means of the mixture components based on speech from a target speaker. In this work we look into the various models (GMM-UBM and GMM-SVM) and their application to speaker verification. In this paper, features vector...

We propose the “generalized min-max” (GMM) kernel as a measure of data similarity, where data vectors can have both positive and negative entries. GMM is positive definite as there is an associate hashing method named “generalized consistent weighted sampling” (GCWS) which linearizes this (nonlinear) kernel. A natural competitor of GMM is the radial basis function (RBF) kernel, whose correspond...

Accurately predicting and inferring a driver’s decision to brake is critical for designing warning systems and avoiding collisions. In this paper we focus on predicting a driver’s intent to brake in car-following scenarios from a perceptiondecision-action perspective according to his/her driving history. A learning-based inference method, using onboard data from CANBus, radar and cameras as exp...

Gaussian Mixture Model (GMM) is an efficient method for parametric clustering. However, traditional GMM can’t perform clustering well on data set with complex structure such as images. In this paper, kernel trick, successfully used by SVM and kernel PCA, is introduced into EM algorithm for solving parameter estimation of GMM, which is so called kernel GMM (kGMM). The basic idea of kernel GMM is...