In earlier studies, we employed a large prosodic feature vector to assess the quality of L2 learner’s utterances with respect to sentence melody and rhythm. In this paper, we combine these features with two standard approaches in paralinguistic analysis: (1) features derived from a Gaussian Mixture Model used as Universal Background Model (GMM-UBM), and (2) openSMILE, an open-source toolkit for...

A spectral conversion method using multiple Gaussian Mixture Models (GMMs) based on the Bayesian framework is proposed. A typical spectral conversion framework is based on a GMM. However, in this conventional method, a GMM-appropriate number of mixtures is dependent on the amount of training data, and thus the number of mixtures should be determined beforehand. In the proposed method, the varia...

Gaussian Mixture Model (GMM) computation is known to be one of the most computation-intensive components of speech recognition. In our previous work, context-independent model based GMM selection (CIGMMS) was found to be an effective way to reduce the cost of GMM computation without significant loss in recognition accuracy. In this work, we propose three methods to further improve the performan...

An efficient, and intuitive algorithm is presented for the identification of speakers from a long dataset (like YouTube long discussion, Cocktail party recorded audio or video).The goal of automatic speaker identification is to identify the number of different speakers and prepare a model for that speaker by extraction, characterization and speaker-specific information contained in the speech s...

This paper describes the derivation of a sequence kernel that transforms speech utterances into probabilistic vectors for classification in an expanded feature space. The sequence kernel is built upon a set of Gaussian basis functions, where half of the basis functions contain speaker specific information while the other half implicates the common characteristics of the competing background spe...

Methods for missing-feature reconstruction substitute noisecorrupted features with clean-speech estimates calculated based on reliable information found in the noisy speech signal. Gaussian mixture model (GMM) based reconstruction has conventionally focussed on reliable information present in a single frame. In this work, GMM-based reconstruction is applied on windows that span several time fra...

This project centers on the investigation of appl-ying Gaussian Mixture Model (GMM) to supervised learning based on the Maximum Lik-elihood (ML) estimation using Expectation Maximization (EM). As learnt, the statistical modeling methods manipulate probabilities dire-ctly, thus giving more sophisticated description over the actual world with its disadvantage of the expensive computational comple...

—Gaussian Mixture Models (GMMs) are the most popular techniques in background modeling but present some limitations when some dynamic changes occur like camera jitter, illumination changes, movement in the background. Furthermore, the GMM are initialized using a training sequence which may be noisy and/or insufficient to model correctly the background. All these critical situations generate fal...

in this paper, a new method is presented for the detection of defects in random textures. in the training stage, the feature vectors of the normal textures’ images are extracted by using the optimal response of gabor wavelet filters, and their probability density is estimated by means of the gaussian mixture model (gmm). in the testing stage, similar to the previous stage,at  first, the feature...

Today, digital audio applications are part of our everyday lives. Automatic audio classification is very useful in audio indexing; content based audio retrieval and online audio distribution. The accuracy of the classification relies on the strength of the features and classification scheme. In this work both, time domain and frequency domain features are extracted from the input signal. Time d...