• 전자공학회논문지B
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• 제30B권2호
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• pp.51-58
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• 1993

This paper describes the development of a speaker-dependent isolated word recognizer as applied to voice dialing in a car noise environment. for this purpose, several methods to improve performance under such condition are evaluated using database collected in a small car moving at 100km/h The main features of the recognizer are as follow: The endpoint detection error can be reduced by using the magnitude of the signal which is inverse filtered by the AR model of the background noise, and it can be compensated by using variants of the DTW algorithm. To remove the noise, an autocorrelation subtraction method is used with the constraint that residual energy obtainable by linear predictive analysis should be positive. By using the noise rubust distance measure, distortion of the feature vector is minimized. The speech recognizer is implemented using the Motorola DSP56001(24-bit general purpose digital signal processor). The recognition database is composed of 50 Korean names spoken by 3 male speakers. The recognition error rate of the system is reduced to 4.3% using a single reference pattern for each word and 1.5% using 2 reference patterns for each word.

• 한국음향학회:학술대회논문집
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• 한국음향학회 1998년도 제15회 음성통신 및 신호처리 워크샵(KSCSP 98 15권1호)
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• pp.87-92
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• 1998

In general, the synthesis unit database was constructed by recording isolated word. In that case, each boundary of word has typical prosodic pattern like a falling intonation or preboundary lengthening. To get natural synthetic speech using these kinds of database, we must artificially distort original speech. However, that artificial process rather resulted in unnatural, unintelligible synthetic speech due to the excessive prosodic modification on speech signal. To overcome these problems, we gathered thousands of sentences for synthesis database. To make a phone level synthesis unit, we trained speech recognizer with the recorded speech, and then segmented phone boundaries automatically. In addition, we used laryngo graph for the epoch detection. From the automatically generated synthesis database, we chose the best phone and directly concatenated it without any prosody processing. To select the best phone among multiple phone candidates, we used prosodic information such as break strength of word boundaries, phonetic contexts, cepstrum, pitch, energy, and phone duration. From the pilot test, we obtained some positive results.

• 음성과학
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• 제11권4호
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• pp.43-52
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• 2004

Model parameters in HMM based speech recognition systems are normally estimated using Maximum Likelihood Estimation(MLE). The MLE method is based mainly on the principle of statistical data fitting in terms of increasing the HMM likelihood. The optimality of this training criterion is conditioned on the availability of infinite amount of training data and the correct choice of model. However, in practice, neither of these conditions is satisfied. In this paper, we propose a training algorithm, MCE(Minimum Classification Error), to improve the performance of a speech recognizer detecting mispronunciation of a foreign language. During the conventional MLE(Maximum Likelihood Estimation) training, the model parameters are adjusted to increase the likelihood of the word strings corresponding to the training utterances without taking account of the probability of other possible word strings. In contrast to MLE, the MCE training scheme takes account of possible competing word hypotheses and tries to reduce the probability of incorrect hypotheses. The discriminant training method using MCE shows better recognition results than the MLE method does.

• 대한음성학회지:말소리
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• 제48호
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• pp.107-116
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• 2003

This paper deals with a new parameter for voice detection which is used for many areas of speech engineering such as speech synthesis, speech recognition and speech coding. CV (Coefficient of Variation) of speech spectrum as well as other feature parameters is used for the detection of speech. CV is calculated only in the specific range of speech spectrum. Average magnitude and spectral magnitude are also employed to improve the performance of detector. From the experimental results the proposed voice detector outperformed the conventional energy-based detector in the sense of error measurements.

• 말소리와 음성과학
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• 제2권4호
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• pp.137-143
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• 2010

In this paper, a speech endpoint detection algorithm is proposed. The proposed algorithm is a kind of state transition-based ones for speech detection. To reject short-duration acoustic pulses which can be considered noises, it utilizes duration information of all detected pulses. For the optimization of parameters related with pulse lengths and energy threshold to detect speech intervals, an exhaustive search scheme is adopted while speech recognition rates are used as its performance index. Experimental results show that the proposed algorithm outperforms the baseline state-based endpoint detection algorithm. At 5 dB input SNR for the beamforming input, the word recognition accuracies of its outputs were 78.5% for human voice noises and 81.1% for music noises.

• 정보보호학회논문지
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• 제32권3호
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• pp.501-511
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• 2022

2021년에는 코로나의 여파로 랜섬웨어를 활용한 공격이 유행했으며 그 수는 매년 급증하고 있다. 그 중 파워쉘은 랜섬웨어에 주요 기술로 사용되고 있어 파워쉘 기반 악성코드 탐지 기법의 필요성은 증가하고 있으나 기존의 탐지기법은 난독화가 적용된 스크립트를 탐지하지 못하거나 역난독화에 시간이 오래 소요되는 한계가 존재한다. 이에 본 논문에서는 간단하고 빠른 역난독화 처리과정, Word2Vec과 CNN(Convolutional Neural Network)으로 구성되어 스크립트의 의미를 학습하고 특징을 추출해 악성 여부를 판단할 수 있는 딥러닝 기반의 분류 모델을 제안한다. 2021 사이버보안 AI/빅데이터 활용 경진대회의 AI 기반 파워쉘 악성 스크립트 탐지 트랙에서 제공된 1400개의 악성코드와 8600개의 정상 스크립트를 이용하여 제안한 모델을 테스트한 결과 기존보다 5.04배 빠른 역난독화 실행시간, 100%의 역난독화 성공률, 0.01의 FPR(False Positve Rate), 0.965의 TPR(True Positive Rate)로 악성코드를 빠르고 효과적으로 탐지함을 보인다.

• 융합정보논문지
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• 제12권2호
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• pp.47-53
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• 2022

최근 지능화된 보안 패러다임의 변화에 따라, 다양한 정보보안 시스템에서 발생하는 각종 정보를 인공지능 기반 이상탐지에 적용하기 위한 연구가 증가하고 있다. 따라서 본 연구는 로그와 같은 시계열 데이터를 수치형 특성인 벡터로 변환하기 위하여 딥러닝 기반 Word2Vec 모델의 CBOW와 Skip-gram 추론 방식과 동시발생 빈도 기반 통계 방식을 사용하여 공개된 ADFA 시스템콜 데이터에 대하여, 벡터의 차원, 시퀀스 길이 및 윈도우 사이즈를 고려한 다양한 임베딩 벡터로의 변환에 대한 실험을 진행하였다. 또한 임베딩 모델로 생성된 벡터를 입력으로 하는 GRU 기반 이상 탐지 모델을 통해 탐지 성능뿐만 아니라 사용된 임베딩 방법들의 성능을 비교 평가하였다. 통계 모델에 비해 추론 기반 모델인 Skip-gram이 특정 윈도우 사이즈나 시퀀스 길이에 치우침 없이 좀 더 안정되게(stable) 성능을 유지하여, 시퀀스 데이터의 각 이벤트들을 임베딩 벡터로 만드는데 더 효과적임을 확인하였다.

• 인지과학
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• 제12권3호
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• pp.19-28
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• 2001

본 논문에서는 한국어문서에서 음차표기된 외래어를 자동적으로 인식 및 추출하는 알고리즘을 제안한다. 제안된 방법에서는 음차표기된 외래어 인식 및 추출 문제를 음절태깅문제로 변환한다. 음절태깅문제는 주어진 단어 내의 음절들에 대하여 순수 한국어를 구성하는 음절인지 또는 음차표기된 외래어를 구성하는 음절인지를 태깅하는 작업으로 정의된다. 이를 위하여. 주어진 어절 내의 음절의 나열을 순수 한국어 음절을 표현하는 상태와 외래어 음절을 표현하는 상태의 이진 상태(binary state)로 모델링한 은닉 마르코프 모델을 이용한다. 제안된 방법은 기존 연구에 비하여 높은 재현율과 정확률로 음차표기된 외래어를 인식 및 추출하였다.

• 말소리와 음성과학
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• 제2권1호
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• pp.59-68
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• 2010

This study addresses the issue of whether Korean(L1)-English(L2) non-proficient bilinguals are affected by the native coda-neutralization process when recognizing words in English continuous speech. Korean phonological rules require that if liaison occurs between 'words', then coda-neutralization process must come before the liaison process, which results in liaison-consonants being coda-neutralized ones such as /b/, /d/, or /g/, rather than non-neutralized ones like /p/, /t/, /k/, /$t{\int}$/, /$d_{\Im}$/, or /s/. Consequently, if Korean listeners apply their native coda-neutralization rules to English speech input, word detection will be easier when coda-neutralized consonants precede target words than when non-neutralized ones do. Word-spotting and word-monitoring tasks were used in Experiment 1 and 2, respectively. In both experiments, listeners detected words faster and more accurately when vowel-initial target words were preceded by coda-neutralized consonants than when preceded by coda non-neutralized ones. The results show that Korean listeners exploit their native phonological process when processing English, irrespective of whether the native process is appropriate or not.

• 한국마린엔지니어링학회:학술대회논문집
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• 한국마린엔지니어링학회 2005년도 후기학술대회논문집
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• pp.204-205
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• 2005

The following instructions give you basic guidelines for preparing camera-ready papers for the proceeding of the KOSME. We recommend you to use the HWP word processor.