• Title, Summary, Keyword: feature transformation

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Speaker Adaptation Using ICA-Based Feature Transformation

  • Jung, Ho-Young;Park, Man-Soo;Kim, Hoi-Rin;Hahn, Min-Soo
    • ETRI Journal
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    • v.24 no.6
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    • pp.469-472
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    • 2002
  • Speaker adaptation techniques are generally used to reduce speaker differences in speech recognition. In this work, we focus on the features fitted to a linear regression-based speaker adaptation. These are obtained by feature transformation based on independent component analysis (ICA), and the feature transformation matrices are estimated from the training data and adaptation data. Since the adaptation data is not sufficient to reliably estimate the ICA-based feature transformation matrix, it is necessary to adjust the ICA-based feature transformation matrix estimated from a new speaker utterance. To cope with this problem, we propose a smoothing method through a linear interpolation between the speaker-independent (SI) feature transformation matrix and the speaker-dependent (SD) feature transformation matrix. From our experiments, we observed that the proposed method is more effective in the mismatched case. In the mismatched case, the adaptation performance is improved because the smoothed feature transformation matrix makes speaker adaptation using noisy speech more robust.

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Simultaneous optimization method of feature transformation and weighting for artificial neural networks using genetic algorithm : Application to Korean stock market

  • Kim, Kyoung-jae;Ingoo Han
    • Proceedings of the Korea Inteligent Information System Society Conference
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    • pp.323-335
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    • 1999
  • In this paper, we propose a new hybrid model of artificial neural networks(ANNs) and genetic algorithm (GA) to optimal feature transformation and feature weighting. Previous research proposed several variants of hybrid ANNs and GA models including feature weighting, feature subset selection and network structure optimization. Among the vast majority of these studies, however, ANNs did not learn the patterns of data well, because they employed GA for simple use. In this study, we incorporate GA in a simultaneous manner to improve the learning and generalization ability of ANNs. In this study, GA plays role to optimize feature weighting and feature transformation simultaneously. Globally optimized feature weighting overcome the well-known limitations of gradient descent algorithm and globally optimized feature transformation also reduce the dimensionality of the feature space and eliminate irrelevant factors in modeling ANNs. By this procedure, we can improve the performance and enhance the generalisability of ANNs.

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Nonlinear Feature Transformation and Genetic Feature Selection: Improving System Security and Decreasing Computational Cost

  • Taghanaki, Saeid Asgari;Ansari, Mohammad Reza;Dehkordi, Behzad Zamani;Mousavi, Sayed Ali
    • ETRI Journal
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    • v.34 no.6
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    • pp.847-857
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    • 2012
  • Intrusion detection systems (IDSs) have an important effect on system defense and security. Recently, most IDS methods have used transformed features, selected features, or original features. Both feature transformation and feature selection have their advantages. Neighborhood component analysis feature transformation and genetic feature selection (NCAGAFS) is proposed in this research. NCAGAFS is based on soft computing and data mining and uses the advantages of both transformation and selection. This method transforms features via neighborhood component analysis and chooses the best features with a classifier based on a genetic feature selection method. This novel approach is verified using the KDD Cup99 dataset, demonstrating higher performances than other well-known methods under various classifiers have demonstrated.

An Ontology - based Transformation Method from Feature Model to Class Model (온톨로지 기반 Feature 모델에서 Class 모델로의 변환 기법)

  • Kim, Dong-Ri;Song, Chee-Yang;Kang, Dong-Su;Baik, Doo-Kwon
    • Journal of the Korea Society of Computer and Information
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    • v.13 no.5
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    • pp.53-67
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    • 2008
  • At present, for reuse of similar domains between feature model and class model. researches of transformation at the model level and of transformation using ontology between two models are being made. but consistent transformation through metamodel is not made. And the factors of modeling transformation targets are not sufficient, and especially, automatic transformation algorithm and supporting tools are not provided so reuse of domains between models is not activated. This paper proposes a method of transformation from feature model to class model using ontology on the metamodel. For this, it re-establishes the metamodel of feature model, class model, and ontology, and it defines the properties of modelling factors for each metamodel. Based on the properties, it defines the profiles of transformation rules between feature mndel and ontology, and between ontology and class model, using set theory and propositional calculus. For automation of the transformation, it creates transformation algorithm and supporting tools. Using the proposed transformation rules and tools, real application is made through Electronic Approval System. Through this, it is possible to transform from the existing constructed feature model to the class model and to use it again for a different development method. Especially, it is Possible to remove ambiguity of semantic transformation using ontology, and automation of transformation maintains consistence between models.

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Speaker Adaptation using ICA-based Feature Transformation (ICA 기반의 특징변환을 이용한 화자적응)

  • Park ManSoo;Kim Hoi-Rin
    • MALSORI
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    • no.43
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    • pp.127-136
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    • 2002
  • The speaker adaptation technique is generally used to reduce the speaker difference in speech recognition. In this work, we focus on the features fitted to a linear regression-based speaker adaptation. These are obtained by feature transformation based on independent component analysis (ICA), and the transformation matrix is learned from a speaker independent training data. When the amount of data is small, however, it is necessary to adjust the ICA-based transformation matrix estimated from a new speaker utterance. To cope with this problem, we propose a smoothing method: through a linear interpolation between the speaker-independent (SI) feature transformation matrix and the speaker-dependent (SD) feature transformation matrix. We observed that the proposed technique is effective to adaptation performance.

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Feature Transformation based Music Retrieval System

  • Heo, Jung-Im;Yang, Jin-Mo;Kim, Dong-Hyun;Yoon, Kyoung-Ro;Kim, Won-Il
    • International Journal of Fuzzy Logic and Intelligent Systems
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    • v.8 no.3
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    • pp.192-195
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    • 2008
  • People have tendency of forgetting music title, though they easily remember particular part of music. If a music search system can find the title through a part of melody, this will provide very convenient interface to users. In this paper, we propose an algorithm that enables this type of search using feature transformation function. The original music is transformed to new feature information with sequential melodies. When a melody that is a part of search music is given to the system, the music retrieval system searches the music similar to the feature information of the melody. Moreover, this transformation function can be easily extended to various music recognition systems.

Spectral Feature Transformation for Compensation of Microphone Mismatches

  • Jeong, So-Young;Oh, Sang-Hoon;Lee, Soo-Young
    • The Journal of the Acoustical Society of Korea
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    • v.22 no.4E
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    • pp.150-154
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    • 2003
  • The distortion effects of microphones have been analyzed and compensated at mel-frequency feature domain. Unlike popular bias removal algorithms a linear transformation of mel-frequency spectrum is incorporated. Although a diagonal matrix transformation is sufficient for medium-quality microphones, a full-matrix transform is required for low-quality microphones with severe nonlinearity. Proposed compensation algorithms are tested with HTIMIT database, which resulted in about 5 percents improvements in recognition rate over conventional CMS algorithm.

Robust Histogram Equalization Using Compensated Probability Distribution

  • Kim, Sung-Tak;Kim, Hoi-Rin
    • MALSORI
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    • v.55
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    • pp.131-142
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    • 2005
  • A mismatch between the training and the test conditions often causes a drastic decrease in the performance of the speech recognition systems. In this paper, non-linear transformation techniques based on histogram equalization in the acoustic feature space are studied for reducing the mismatched condition. The purpose of histogram equalization(HEQ) is to convert the probability distribution of test speech into the probability distribution of training speech. While conventional histogram equalization methods consider only the probability distribution of a test speech, for noise-corrupted test speech, its probability distribution is also distorted. The transformation function obtained by this distorted probability distribution maybe bring about miss-transformation of feature vectors, and this causes the performance of histogram equalization to decrease. Therefore, this paper proposes a new method of calculating noise-removed probability distribution by using assumption that the CDF of noisy speech feature vectors consists of component of speech feature vectors and component of noise feature vectors, and this compensated probability distribution is used in HEQ process. In the AURORA-2 framework, the proposed method reduced the error rate by over $44\%$ in clean training condition compared to the baseline system. For multi training condition, the proposed methods are also better than the baseline system.

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Effective Combination of Temporal Information and Linear Transformation of Feature Vector in Speaker Verification (화자확인에서 특징벡터의 순시 정보와 선형 변환의 효과적인 적용)

  • Seo, Chang-Woo;Zhao, Mei-Hua;Lim, Young-Hwan;Jeon, Sung-Chae
    • Phonetics and Speech Sciences
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    • v.1 no.4
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    • pp.127-132
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    • 2009
  • The feature vectors which are used in conventional speaker recognition (SR) systems may have many correlations between their neighbors. To improve the performance of the SR, many researchers adopted linear transformation method like principal component analysis (PCA). In general, the linear transformation of the feature vectors is based on concatenated form of the static features and their dynamic features. However, the linear transformation which based on both the static features and their dynamic features is more complex than that based on the static features alone due to the high order of the features. To overcome these problems, we propose an efficient method that applies linear transformation and temporal information of the features to reduce complexity and improve the performance in speaker verification (SV). The proposed method first performs a linear transformation by PCA coefficients. The delta parameters for temporal information are then obtained from the transformed features. The proposed method only requires 1/4 in the size of the covariance matrix compared with adding the static and their dynamic features for PCA coefficients. Also, the delta parameters are extracted from the linearly transformed features after the reduction of dimension in the static features. Compared with the PCA and conventional methods in terms of equal error rate (EER) in SV, the proposed method shows better performance while requiring less storage space and complexity.

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Control of Morphological Development and Transformation of Curves (곡선의 형태학적 성장과 변환의 제어 방법)

  • Lee, Joo-Haeng;Park, Hyung-Jun
    • Korean Journal of Computational Design and Engineering
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    • v.12 no.5
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    • pp.354-365
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    • 2007
  • We present novel methods to generate a sequence of shapes that represents the pattern of morphological development or transformation of Bezier curves. The presented methods utilize the intrinsic geometric structures of a Bezier curve that are derived from rib and fan decomposition (RFD). Morphological development based on RFD shows a characteristic pattern of structural growth of a Bezier curve, which is the direct consequence of development path defined by fans. Morphological transformation based RFD utilizes development patterns of source and target curves to mimic the theory of evolutionary developmental biology: although the source and target curves are quite different in shapes, we can easily find similarities in their younger shapes, which makes it easier to set up feature correspondences for blending them. We also show that further controls on base transformation for intensity of feature blending, and extrapolation can compensate the immaturity of blended curves. We demonstrate the experimental results where transformation patterns are smoother and have unique geometric style that cannot be generated using conventional methods based on multi-linear blending.