The voice conversion algorithm based on the Gaussian mixture model (GMM) has also been proposed by Stylianou et al. In this algorithm, the acoustic space of a speaker is represented continuously. In this paper, we apply this GMMbased voice conversion algorithm to STRAIGHT proposed by Kawahara et al., which is recognized as a high quality vocoder. In order to evaluate this voice conversion algor...

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...

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)-...

In this paper, we present a kernel trick embedded Gaussian Mixture Model (GMM), called kernel GMM. The basic idea is to embed kernel trick into EM algorithm and deduce a parameter estimation algorithm for GMM in feature space. Kernel GMM could be viewed as a Bayesian Kernel Method. Compared with most classical kernel methods, the proposed method can solve problems in probabilistic framework. Mo...

Variability in speech due to dialect is a major factor limiting speech system performance for speech recognition, spoken document retrieval, and dialog systems. In this study, we propose a novel discriminative algorithm to improve dialect classification for unsupervised spontaneous speech in Arabic. No transcripts are used for either training or testing, and all data are spontaneous speech. The...

This study is mainly focusing on the problem of spacecraft close-range proximity with obstacle avoidance in presence complex shape. A novel Gaussian mixture model–based nonsingular terminal sliding mode control (GMM-NTSMC) proposed. achieved by developing GMM-based potential function a switching surface NTSMC. It theoretically proved that closed-loop system globally stable. The main contributio...

Abstract: Stochastic models such as mixture models, graphical models, Markov random fields and hidden Markov models have key role in probabilistic data analysis. In this paper, we used Gaussian mixture model to the pixels of an image. The parameters of the model were estimated by EM-algorithm.   In addition pixel labeling corresponded to each pixel of true image was made by Bayes rule. In fact,...

Instead of the expansion of the information retrieval systems, the music information retrieval domain is still an open one. In this context, the singing voice classification is a promised trend. In this paper, we shall present our experiments concerning the classification of singers according to their voice type, and their voice quality. Some experiments were carried in which two sets of parame...

In this paper, we describe a novel model training method for one-to-many eigenvoice conversion (EVC). One-to-many EVC is a technique for converting a specific source speaker’s voice into an arbitrary target speaker’s voice. An eigenvoice Gaussian mixture model (EVGMM) is trained in advance using multiple parallel data sets consisting of utterance-pairs of the source speaker and many pre-stored ...

In this paper, we present a statistical model-based speech enhancement technique using acoustic environment classification supported by a Gaussian mixture model (GMM). In the data training stage, the principal parameters of the statistical model-based speech enhancement algorithm such as the weighting parameter in the decision-directed (DD) method, the long-term smoothing parameter of the noise...