Gaussian Mixture Model-based Method Research Articles

Multimodal voice conversion based on non-negative matrix factorization

A multimodal voice conversion (VC) method for noisy environments is proposed. In our previous non-negative matrix factorization (NMF)-based VC method, source and target exemplars are extracted from parallel training data, in which the same texts are uttered by the source and target speakers. The input source signal is then decomposed into source exemplars, noise exemplars, and their weights. Then, the converted speech is constructed from the target exemplars and the weights related to the source exemplars. In this study, we propose multimodal VC that improves the noise robustness of our NMF-based VC method. Furthermore, we introduce the combination weight between audio and visual features and formulate a new cost function to estimate audio-visual exemplars. Using the joint audio-visual features as source features, VC performance is improved compared with that of a previous audio-input exemplar-based VC method. The effectiveness of the proposed method is confirmed by comparing its effectiveness with that of a conventional audio-input NMF-based method and a Gaussian mixture model-based method.

Model-based classification methods of global patterns in dermoscopic images.

In this paper different model-based methods of classification of global patterns in dermoscopic images are proposed. Global patterns identification is included in the pattern analysis framework, the melanoma diagnosis method most used among dermatologists. The modeling is performed in two senses: first a dermoscopic image is modeled by a finite symmetric conditional Markov model applied to L∗a∗b∗ color space and the estimated parameters of this model are treated as features. In turn, the distribution of these features are supposed that follow different models along a lesion: a Gaussian model, a Gaussian mixture model, and a bag-of-features histogram model. For each case, the classification is carried out by an image retrieval approach with different distance metrics. The main objective is to classify a whole pigmented lesion into three possible patterns: globular, homogeneous, and reticular. An extensive evaluation of the performance of each method has been carried out on an image database extracted from a public Atlas of Dermoscopy. The best classification success rate is achieved by the Gaussian mixture model-based method with a 78.44% success rate in average. In a further evaluation the multicomponent pattern is analyzed obtaining a 72.91% success rate.