• The Journal of the Acoustical Society of Korea
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• v.28 no.3
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• pp.268-272
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• 2009

In this paper, we propose a Gaussian Mixture Model (GMM) based incremental robust adaptation with a forgetting factor for the speaker verification. Speaker recognition system uses a speaker model adaptation method with small amounts of data in order to obtain a good performance. However, a conventional adaptation method has vulnerable to the outlier from the irregular utterance variations and the presence noise, which results in inaccurate speaker model. As time goes by, a rate in which new data are adapted to a model is reduced. The proposed algorithm uses an incremental robust adaptation in order to reduce effect of outlier and use forgetting factor in order to maintain adaptive rate of new data on GMM based speaker model. The incremental robust adaptation uses a method which registers small amount of data in a speaker recognition model and adapts a model to new data to be tested. Experimental results from the data set gathered over seven months show that the proposed algorithm is robust against outliers and maintains adaptive rate of new data.

• Journal of Internet Computing and Services
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• v.5 no.2
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• pp.69-73
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• 2004

A speaker model In speaker recognition system is to be trained from a large data set gathered in multiple sessions. Large data set requires large amount of memory and computation, and moreover it's practically hard to make users utter the data inseveral sessions. Recently the incremental adaptation methods are proposed to cover the problems, However, the data set gathered from multiple sessions is vulnerable to the outliers from the irregular utterance variations and the presence of noise, which result in inaccurate speaker model. In this paper, we propose an incremental robust adaptation method to minimize the influence of outliers on Gaussian Mixture Madel based speaker model. The robust adaptation is obtained from an incremental version of M-estimation. Speaker model is initially trained from small amount of data and it is adapted recursively with the data available in each session, Experimental results from the data set gathered over seven months show that the proposed method is robust against outliers.

• Proceedings of the Korea Contents Association Conference
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• 2010.05a
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• pp.466-468
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• 2010

본 논문에서는 다양한 컬러모델 영상을 대상으로 히스토그램의 차이를 이용한 장면 전환 검출 결과를 비교한다. 임계값은 히스토그램의 변화에 따라 변화하는 적응적 임계값 설정 방법을 사용하여 오검출과 미검출의 확률을 줄였다. 점진적인 장면 전환에도 강인한 검출을 위하여 탐색 구간의 변화와 히스토그램의 변화가 급격하게 자주 일어나는 구간의 히스토그램의 변화율을 이용하여 장면 전환을 검출 하는 방법을 제안 하였다.

• Journal of the Institute of Convergence Signal Processing
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• v.11 no.3
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• pp.183-188
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• 2010

In this paper we propose a detection method of the medial axis of the continuous stripes on the LoG scale-space. Our method detects the medial axis of continuous stripes iteratively by varying the scale of LoG operator. Small-scale LoG operator detects two +/- pole pairs centered on the edge positions of stripe by the zero-crossing detection. The more increase the scale of LoG scale-space, the more close two poles to the medial axis of stripe. The medial axis of continuous stripe is the position where two poles is overlapped. The proposed method detected robustly the medial axis of continuous stripes stronger than the thinning methods used to binary image.