We introduce a new method for data clustering based on a particular Gaussian mixture model (GMM). Each cluster of data, modeled as a GMM into an input space, is interpreted as a hyperplane in a high dimensional mapping space where the underlying coefficients are found by solving a quadratic programming (QP) problem. The main contributions of this work are (1) an original probabilistic framework...

Voice conversion (VC) can be seen as a powerful technology for customizing Text-to-Speech (TTS) systems. This paper deals with the integration of a VC method based on Gaussian Mixture Model (GMM) in a TTS system. In this framework, an algorithm that enables complexity reduction of the VC processing is proposed. The main idea is to restrict the conversion function to the most representative comp...

Diagnosis of corneal diseases is possible by measuring and evaluation of corneal thickness in different layers. Thus, the need for precise segmentation of corneal layer boundaries is inevitable. Obviously, manual segmentation is time-consuming and imprecise. In this paper, the Gaussian mixture model (GMM) is used for automatic segmentation of three clinically important corneal boundaries on opt...

Electrolaryngeal (EL) speech produced by a laryngectomee using an electrolarynx to mechanically generate artificial excitation sounds severely suffers from unnatural fundamental frequency (F0) patterns caused by monotonic excitation sounds. To address this issue, we have previously proposed EL speech enhancement systems using statistical F0 pattern prediction methods based on a Gaussian Mixture...

This paper proposes a voice morphing system for people suffering from Laryngectomy, which is the surgical removal of all or part of the larynx or the voice box, particularly performed in cases of laryngeal cancer. A primitive method of achieving voice morphing is by extracting the source's vocal coefficients and then converting them into the target speaker's vocal parameters. In this ...

Identification of de novo minisatellite mutations in the offspring of parents exposed to mutagenic agents offers a potentially sensitive measure of germ line genetic events induced by ionizing radiation and genotoxic chemicals. Germ line minisatellite mutations (GMM) are usually detected by hybridizing Southern blots of unamplified size-fractionated genomic DNA with minisatellite probes. Howeve...

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...

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)-...