• 말소리와 음성과학
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• 제8권4호
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• pp.103-113
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• 2016

This paper aims to propose articulatory features as novel predictors for automatic pronunciation assessment of English produced by Korean learners. Based on the distinctive feature theory, where phonemes are represented as a set of articulatory/phonetic properties, we propose articulatory Goodness-Of-Pronunciation(aGOP) features in terms of the corresponding articulatory attributes, such as nasal, sonorant, anterior, etc. An English speech corpus spoken by Korean learners is used in the assessment modeling. In our system, learners' speech is forced aligned and recognized by using the acoustic and pronunciation models derived from the WSJ corpus (native North American speech) and the CMU pronouncing dictionary, respectively. In order to compute aGOP features, articulatory models are trained for the corresponding articulatory attributes. In addition to the proposed features, various features which are divided into four categories such as RATE, SEGMENT, SILENCE, and GOP are applied as a baseline. In order to enhance the assessment modeling performance and investigate the weights of the salient features, relevant features are extracted by using Best Subset Selection(BSS). The results show that the proposed model using aGOP features outperform the baseline. In addition, analysis of relevant features extracted by BSS reveals that the selected aGOP features represent the salient variations of Korean learners of English. The results are expected to be effective for automatic pronunciation error detection, as well.

• 비파괴검사학회지
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• 제32권4호
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• pp.418-430
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• 2012

본 논문은 재료의 표면검사와 음향특성 측정이 가능한 마이크로/나노 비파괴평가 기술을 소개한다. 이들 기술로 초음파원자현미경과 초음파현미경의 원리와 특징 그리고 응용분야에 대해서 기술하였다. 특히, 이들 기술은 표면과 표면직하의 이미지 관찰 외에도 음향특성을 측정하여 마이크로/나노 구조물 혹은 표면에서의 기계적인 물성평가가 가능한 기술이다. 따라서 기존 비파괴분야와 함께 첨단 산업분야에 있어 마이크로/나노 비파괴평가의 적용과 기술 개발이 향후 폭넓게 가능할 것으로 판단된다.

• 한국전기전자재료학회:학술대회논문집
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• 한국전기전자재료학회 2000년도 하계학술대회 논문집
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• pp.585-591
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• 2000

The wavelet transform is the most recent technique for processing signals with time-varying spectra. In this paper, the wavelet transform is utilized to improved the assessment and multi-resolution analysis of acoustic emission signals generating in partial discharge. This paper especially deals with the assessment of process statistical parameter using the features extracted from the wavelet coefficients of measured acoustic emission signals in case of applied voltage 20[kv]. Since the parameter assessment using all wavelet coefficients will often turn out leads to inefficient or inaccurate results, we selected that level-3 stage of multi decomposition in discrete wavelet transform. We applied FIR(Finite Impulse Response)digital filter algorithm in discrete to suppression for random noise. The white noise be included high frequency component denoised as decomposition of discrete wavelet transform level-3. We make use of the feature extraction parameter namely, maximum value of acoustic emission signal, average value, dispersion, skewness, kurtosis, etc. The effectiveness of this new method has been verified on ability a diagnosis transformer go through feature extraction in stage of acting(the early period, the last period) .

• 대한음성학회지:말소리
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• 제21_24호
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• pp.43-89
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• 1992

In this thesis, the prominent aspect of word accent in standard Korean is studied by auditory test and acoustic analysis experiment. The definition of 'accent' is, following Hoyoung Lee's discussion(1990), to be described as 'the means whereby a focused part of an utterance is made to stand out in order to concentrate the hearer's attention on it.' That is to say, the ten of 'accent' may be described in terms of phonological phenomenon and the accented syllable can be phonetically prominent as the result of those phonological process. Prosodic features may have different characteristics in different languages whether they contain linguistically important functions or not. Thus the characteristics of word accent in standard Korean will be determined as the content and trait of prosodic features. Following this viewpoint, present study looked over prosodic features which may effect the characteristics of word accent in standard Korean, through systematic experimental procedure. And the result of this experiment has been verified by statistical method, the T-test, for the purpose of identifying the relatedness among prosodic features(parameters). This thesis, therefore, aimed to investigate the intrinsic acoustic and physical qualities of the word accent in standard Korean. Nonsense words composed by 'mal' and 'ma' which can be divided into 'heavy syllable' and 'light syllable' quoted from Hyman(1975) have been classified into 28 types with respect to syllable numbers(2 syl., 3 sy1., 4 syl.) and these words have become the target of auditory test and acoustic experiment. As the result of those experimental Procedures, the word accent in standard Korean may be said that it has a tendency of fixing first two syllables regardless of syllable numbers. The syllable types of HH, HL, LL in the first two syllables may be prominent at first syllable and the type of H may be at second syllable. Various prosodic features(parameters) including duration, intensity, and Fo(purely phonetic terms) were also strengthened in those positions. The result of this experiment can be cleared up like these : 1. The most important feature is proved as 'duration', the feature of intensity resulted in more subsidiary one than the feature of duration. 2. Fo( fundamental frequency) could be observed as having some coherent contour through almost all syllable types(99 ％), that is, in 2 syllable types, it had rising contour, in 2 syllable types, rising-falling contour, and in 4 syllable types, it contained rising-falling-rising contour. The result of auditory test was different with those contour forms of all Fo surveyed. With respect to these results, the discuss for Fo is determined' to be excluded comparing other features. 3. Finally, this thesis resulted in a decision that the word accent in standard Korean may has fixed(somewhat weaker) accent, especially fixed at first two syllables in almost all words. 4. Various kinds of syllable types related with 2,3,4 syllables, therefore, can be reclassified into 4 types of HH, HL, LH, LL following the concept of accent fixing placement(i.e. first two syllables). In these 4 types, the types of HH, HL, LL were prominent at the position of the first syllable , and the type of LH was prominent at the second syllable otherwise.

• 한국음향학회지
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• 제35권1호
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• pp.63-73
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• 2016

청각기관의 음조체계로 인해 사람들은 일반적으로 주파수 분포에 따라 소리를 듣는다. 그러나 어음인지 측면에서 어음의 음향적 특성이 사람의 뇌에서 어떻게 인식되는지는 여전히 명확하지 않다. 따라서 본 연구에서는 유사한 고주파수 음향적 특성을 갖는 두 개의 어음이 청각 뇌간에서 전기생리학적으로 어떻게 발현되는 지 확인하고자 하였다. 정상 청력을 지닌 20대 성인 33명이 실험에 참여하였다. 자극음으로 두 개의 한국어 단음절 /자/와 /차/, 4개의 주파수로 구성된 톤버스트음(500, 1000, 2000, 4000 Hz)을 사용하여 청성뇌간반응을 얻었다. 연구 결과, 단음절과 톤버스트음 모두 높은 재현성을 보였고, 파형 V는 모든 피검자에게서 잘 발현되었다. 피어슨 상관관계 분석 결과, 3671 ~ 5384 Hz 대역에서 에너지 분포를 갖는 /자/ 음절은 4000 Hz의 톤버스트음과 높은 상관관계를 나타냈다. 그러나 /차/ 음절은 1000 Hz와 2000 Hz의 톤버스트음과 높은 상관성을 보여, 3362~5412 Hz의 음향적 특성과 청각 뇌간에서 생리학적 반응은 일치하지 않았다. 이러한 결과를 바탕으로 사람의 어음인지과정을 면밀히 조사하기 위해 음향-청지각적 매핑후속 연구가 필요하겠다.

• 한국컴퓨터정보학회논문지
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• 제26권10호
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• pp.37-43
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• 2021

말소리의 음향 특징을 이용하여 화자에 대한 중요한 사회, 언어학적 정보를 얻을 수 있는데 그 중 한 가지 핵심 특징은 방언이다. 화자의 방언 사용은 컴퓨터와의 상호작용을 방해하는 주요 요소이다. 방언은 발화의 음소, 음절, 단어, 문장 및 구와 같이 다양한 수준에서 구분할 수 있지만 이를 하나하나 식별하여 방언을 구분하기는 어렵다. 이에 본 논문에서는 음성 데이터의 특성 중 MFCC만 사용하는 경량화된 한국어 방언 분류 모델을 제안한다. 한국인 대화 음성 데이터를 통해 MFCC 특징을 활용하는 최적의 방법을 연구하고, 8가지 머신 러닝 및 딥러닝 분류 모델에서 경기/서울, 강원, 충청, 전라, 경상 5개의 한국어 방언 분류 성능을 비교한다. MFCC를 정규화하는 방법으로 대부분의 분류 모델에서 성능을 향상시켰으며, MFCC를 정규화하기 전 분류 모델의 최고 성능과 비교하여 정확도는 1.07%, F1-score는 2.04% 향상된 성능을 기록하였다.

• 한국정밀공학회지
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• 제20권11호
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• pp.47-54
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• 2003

Research during the past several years has established the effectiveness of acoustic emission (AE)-based sensing methodologies for machine condition analysis and process. AE has been proposed and evaluated for a variety of sensing tasks as well as for use as a technique for quantitative studies of manufacturing process. STD11 has been known as difficult-to-cut materials. The micro-grooving machine was developed for this study and the experiments were performed using CBN blade for machining STD11. Evaluating the machining conditions, frequency spectrum analysis of acoustic emission (AE) signals according to each conditions were applied. Fuzzy clustering method for associating the preprocessor outputs with the appropriate decisions was followed by frequency spectrum analysis. FFT is used to decompose AE signal into different frequency bands in time domain, the root mean square (RMS) values extracted from the decomposed signal of each frequency band were used as features.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2002년도 ITC-CSCC -2
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• pp.818-821
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• 2002

In this paper, we propose Neuro-Fuzzy discrimination method of the new and old bill using bill money acoustic data. The concept of the histogram is introduced to improve the processing time into the proposal system. The adaptative filter is used in order to remove the motor sound from an observed bill money acoustic data. The output signal of this adaptive digital filter is converted into not only a spectrum but also a histogram. It became easy that features of the paper money sound were extracted from the bill money acoustic data. The spectral data and the histogram is obtained like this, and it become an input pattern of the neural network(NN). Then, the discrimination result of the NN is finally judged by the fuzzy inferece in the new bill or the exhaustion bill.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 1997년도 추계학술대회 논문집
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• pp.245-248
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• 1997

The distilled water is used for the ultrasonic wave propagating material in the measurements of broadband ultrasonic attenuation (BUA) that is applied in industrial and medical applications, The acoustic impedance of water is significantly changed with its temperature. Therefore, the quantitative evaluation of BUA with temperature and the ultrasonic wave propagating distance is highly needed. In this study, we evaluated the variation of attenuation with change in temperature. To measure the variation of BUA in the low frequency region at the temperatures, 27$^{\circ}C$, 29$^{\circ}C$, and 31$^{\circ}C$, we tested the Plyethylene, Teflon, MC-Nylon, Urethane specimens and analyzed the center frequency, frequency bandwidth, spectral peak amplitude. The results showed that BUA value appeared to be lower with increasing temperature. This may be due to the fact that the frequency feature of ultrasonic wave is affected by not only the specific gravity, acoustic impedence, but material crystalline, porosity, the distance of ultrasonic wave propagation in water.

• 한국공작기계학회논문집
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• 제15권4호
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• pp.22-28
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• 2006

Detection of exit burr is very important in manufacturing automation. In this paper, acoustic emission(AE) was used to detect the burr formation during milling. By using wavelet transformation, AE data was compressed without unnecessary details. Then the transformed data were used as selected features (inputs) of a back-propagation artificial neural net. In order to validate the proposed scheme, the wavelet based ANN results were compared with cutting condition(cutting speed, feed, depth of cut, etc.) based ANN results.