We develop a new compressive sensing (CS) inversion algorithm by utilizing the Gaussian mixture model (GMM). While the compressive sensing is performed globally on the entire image as implemented in our lensless camera, a lowrank GMM is imposed on the local image patches. This lowrank GMM is derived via eigenvalue thresholding of the GMM trained on the projection of the measurement data, thus l...

In this paper we investigate the Gaussian Mixture Model (GMM) framework for adaptation of context-dependent deep neural network HMM (CD-DNN-HMM) acoustic models. In the previous work an initial attempt was introduced for efficient transfer of adaptation algorithms from the GMM framework to DNN models. In this work we present an extension, further detailed exploration and analysis of the method ...

We propose a class of new robust Generalized Method of Moments (GMM) tests for endogenous structural breaks. The tests are based on supremum, average and exponential functionals derived from robust GMM estimators with bounded influence function. We study the theoretical local robustness properties of the new tests and show that they imply a uniformly bounded asymptotic sensitivity of size and p...

The relationship of geometric morphometrics (GMM) to functional analysis of the same morphological resources is currently a topic of active interest among functional morphologists. Although GMM is typically advertised as free of prior assumptions about shape features or morphological theories, it is common for GMM findings to be concordant with findings from studies based on a-priori lists of s...

This paper proposes a sequence-to-frame dynamic time warping (DTW) combination approach to improve out-ofvocabulary (OOV) spoken term detection (STD) performance gain. The goal of this paper is twofold: first, we propose a method that directly adopts the posterior probability of deep neural network (DNN) and Gaussian mixture model (GMM) as the similarity distance for sequence-to-frame DTW. Seco...

A two-stage classifier is used to improve the classification performance between normal and pathological voices. A primary classification between normal and pathological voices is achieved by the Gaussian mixture model (GMM) log-likelihood scores. For samples that do not meet the thresholds for normal or disordered voice in the GMM, the final decision is made by a higher-order statistics (HOS)-...

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).