• 대한전자공학회논문지SP
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• 제46권3호
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• pp.138-142
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• 2009

본 논문에서는 보이스 피싱 (Voice Pishing) 예방을 위한 알고리즘을 최소 분류 오차 기법 (Minimum Classification Error)을 기반으로 제한하다. 휴대폰으로 전송되어진 신호를 기반으로 3GPP2 Selectable Mode Vocoder (SMV)의 복호화 과정에서 자동적으로 추출되는 중요 특징벡터를 사용하여 Gaussian Mixture Model (GMM)을 구성하고 이를 기반으로 구해지는 로그(Log) 기반의 우도 (Likelihood)를 사용한 변별적 가중치 학습을 사용하여 보이스 피싱 예방을 위한 검출 알고리즘을 제안하다. 실험 결과 제안된 보이스 피싱 알고리즘이 기존의 방법에 비해 우수한 성능을 보인 것을 알 수 있었다.

• ETRI Journal
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• 제30권6호
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• pp.865-867
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• 2008

A conventional environment adaptation for robust speech recognition is usually conducted using transform-based techniques. Here, we present a discriminative adaptation strategy based on a multi-condition-trained model, and propose a new method to provide universal application to a new environment using the environment's specific conditions. Experimental results show that a speech recognition system adapted using the proposed method works successfully for other conditions as well as for those of the new environment.

• International Journal of Computer Science & Network Security
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• 제21권11호
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• pp.89-96
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• 2021

This work is an extension of my work presented a robust and economically efficient method for the Discrimination of four Mung-Beans [1] varieties based on quantitative parameters. Due to the advancement of technology, users try to find the solutions to their daily life problems using smartphones but still for computing power and memory. Hence, there is a need to find the best classifier to classify the Mung-Beans using already suggested features in previous work with minimum memory requirements and computational power. To achieve this study's goal, we take the experiments on various supervised classifiers with simple architecture and calculations and give the robust performance on the most relevant 10 suggested features selected by Fisher Co-efficient, Probability of Error, Mutual Information, and wavelet features. After the analysis, we replace the Artificial Neural Network and Deep learning with a classifier that gives approximately the same classification results as the above classifier but is efficient in terms of resources and time complexity. This classifier is easily implemented in the smartphone environment.