I-vector based recognition is a well-established technique in state-of-the-art speaker and language recognition but its use in dialect and accent classification has received less attention. We represent an experimental study of i-vector based dialect classification, with a special focus on foreign accent detection from spoken Finnish. Using the CallFriend corpus, we first study how recognition ...

Codebook design for vector quantization could be performed using clustering technique. The Gaussian Mixture Modeling (GMM) clustering algorithm involves modeling a statistical distribution by a mixture (or weighted sum) of other distributions. GMM has proven superior efficiency in both time and accuracy and has been used with vector quantization in some applications. This paper introduces a med...

To solve the task of segmenting clusters of nearly identical objects we here present the template rotation expectation maximization (TREM) approach which is based on a generative model. We explore both a non-linear optimization approach for maximizing the loglikelihood and a modification of the standard expectation maximization (EM) algorithm. The non-linear approach is strict template matching...

Our approach to the ImageCLEF 2010 tasks is based on Histogram of Oriented Gradients descriptors (HOG) and Okapi BM25 based text retrieval. We extracted feature vectors to describe the visual content of an image region or the entire image. We trained a Gaussian Mixture Model (GMM) to cluster the feature vectors extracted from the image regions. To represent each image with only one vector we co...

Our recent experiments with Gaussian mixture (GMM) based acoustic models have shown that employing context dependent acoustic models, namely triphones, can greatly improve recognition accuracy [1] in comparison to systems based on context independent units. Significant portion of our research has been aimed at exploring the possibilities of neural networks as acoustic models for speech recognit...

In this paper, we proposed an unsupervised posture modeling method based on Gaussian Mixture Model (GMM). Specifically, each learning posture is described based on its movement features by a set of spatial-temporal interest points (STIPs), salient postures are then clustered from these training samples by an unsupervised algorithm, here we give the comparison of four candidate classification me...

In this paper, we propose a novel voice conversion method called speaker model alignment (SMA), which does not require parallel training speech. Firstly, the source and target speaker models, described by Gaussian mixture model (GMM), are trained, respectively. Then, the transformation function of spectral features is learned by aligning the components of source and target speaker models iterat...

We compare the performance of ve algorithms for vector quan-tisation and clustering analysis: the Self-Organising Map (SOM) and Learning Vector Quantization (LVQ) algorithms of Kohonen, the Linde-Buzo-Gray (LBG) algorithm, the MultiLayer Perceptron (MLP) and the GMM/EM algorithm for Gaussian Mixture Models (GMM). We propose that the GMM/EM provides a better representation of the speech space an...

Boosting is an effecient method to improve the classification performance. Recent theoretical work has shown that the boosting technique can be viewed as a gradient descent search for a good fit in function space. Several authors have applied such viewpoint to solve the density estimation problems. In this paper we generalize such framework to a specific density model – Gaussian Mixture Model (...