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In this paper, we present a novel algorithm for embedding a watermark into a still host image in the DCT domain. Unlike the traditional techniques, we embed a binary image as a watermark into the dc components of the DCT coefficients of 8/spl times/8 blocks. In the proposed algorithm, we incorporate the feature of texture masking and luminance masking of(More)
As powerful theoretical and computational tools, support vector machines (SVMs) have been widely used in pattern classification of many areas. A key issue of applying SVMs to language identification of speech signals is to find a SVM kernel that compares a sequence of feature vectors with others efficiently. In this paper, we introduce a sequence kernel(More)
Methods based on difference between Gaussian mixture models (GMMs) are widely used in speaker clustering. The paper presents a novel pseudo-divergence, the ratio of inter-model dispersion to intra-model dispersion, to characterize the difference between two GMMs. In dispersion, weight, mean and variance, of which a GMM is composed, are involved. Experiments(More)
There are two most popular techniques in pattern recognition, discriminative classifiers and generative model classifiers. Combining them together could improve the performance of the recognition system. We present a novel method for text-independent speaker recognition. This system uses the output of the Gaussian mixture model to adjust the probabilistic(More)
A novel and noise robust front-end based on the combination of spectral noise reduction and probability model-based feature compensation and cepstral mean subtraction (CMS) is proposed. Mel filter-bank outputs can be affected by additive noise primarily because of the vulnerable spectral valleys. An instantaneous Wiener filter is used to improve SNR of the(More)
Gaussian mixture model is an effective method for speaker-independent language identification tasks. Gaussian mixture bigram model integrates bigram time correlation to extend the GMM. A language identification algorithm of GMBM-UBBM is proposed based on GMBM and GMM-UBM and some experiments are conducted using OGI-TS multilanguage telephone speech corpus.(More)
Phonetic inventories differ from language to language. Even when languages have identical phones, the frequencies of occurrence of phones differ across languages. It's difficult to introduce new languages when the language identification system used phones label. The frequencies of occurrence of phones were trained by Gaussian mixture model and vector(More)
This paper is intended to present a novel rough set based approach to identifying base noun phrase (BaseNP). In this approach, we divide the whole task into two ordinal sub tasks: tagging and identifying. We regard BaseNP tagging as a decision + making process, which can be accomplished through rough set theory. What characterizes our tagging procedure is(More)
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