• 아동학회지
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• 제15권2호
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• pp.195-211
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• 1994

This study was designed to examine the effects of family structure (nuclear, extended family) on children's behavioral and emotional problems. The subjects of this study were 650 primary school children aged 8 to 13 living in Seoul and Incheon (324 from nuclear and 326 from extended families). The Children's Behaviour Questionnaire (CBQ: Rutter, 1970) and a questionnaire containing demographic questions and some additional questions were used for instruments. For the analyses, frequencies, descriptives, chi-square, factor analysis, and t-test were used. The results were analysed and explained in their cut-off points, prevalences, differences and factor structures. The cut-off score obtained in this study was 18, and the prevalences with this cut-off score were 6.3% in total subjects, 8.0% in nuclear families, and 4.6% in extended families. The children from extended families showed less behavioral and emotional problems, especially antisocial behaviours, compared with those from nuclear families, and this difference was significant (p<.05). The factor structures of the CBQ were 'antisocial', 'neurotic', 'mannerism' and 'speech' in total subjects, and, in the case of nuclear families, the factor structures were same as in total subjects, but, in the case of extended families, the 'emotional anxiety' factor was included instead of the 'speech' factor.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2003년도 하계종합학술대회 논문집 Ⅳ
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• pp.2439-2442
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• 2003

In this paper, we examine the acoustic characteristics of Korean vowels on pitch alteration utterance. The prosody is known as an indicator of acoustic characteristics of emotions. Also, speech is acoustically differenced according to the emotional variation and environmental variation, although speaker utters the same speech. We analyzed the spectral envelopes and formants from the voiced regions as data points on the speech waveform.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2002년도 하계종합학술대회 논문집(4)
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• pp.377-380
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• 2002

In this paper, speaker and context independent emotion recognition using speech signal is studied. For this purpose, a corpus of emotional speech data recorded and classified according to the emotion using the subjective evaluation were used to make statical feature vectors such as average, standard deviation and maximum value of pitch and energy and to evaluate the performance of the conventional pattern matching algorithms. The vector quantization based emotion recognition system is proposed for speaker and context independent emotion recognition. Experimental results showed that vector quantization based emotion recognizer using MFCC parameters showed better performance than that using the Pitch and energy Parameters.

• 대한음성학회:학술대회논문집
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• 대한음성학회 2007년도 한국음성과학회 공동학술대회 발표논문집
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• pp.67-70
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• 2007

The present paper focuses on the interaction between lexical-semantic information and affective prosody. The previous studies showed that the influence of lexical-semantic information on the affective evaluation of the prosody was relatively clear, but the influence of emotional prosody on the word evaluation remains still ambiguous. In the present, we explore whether affective prosody influence on the evaluation of affective meaning of a word and vice versa, using more ecological stimulus (sentences) than simple words. We asked participants to evaluate the emotional valence of the sentences which were recorded with affective prosody (negative, neutral, and positive) in Experiment 1 and the emotional valence of their prosodies in Experiment 2. The results showed that the emotional valence of prosody can influence on the emotional evaluation of sentences and vice versa. Interestingly, the positive prosody is likely to be more responsible to this interaction.

• 감성과학
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• 제27권2호
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• pp.3-14
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• 2024

감정적 맥락이 기억에 미치는 영향을 이해하는 것은 인간의 인지 능력을 종합적으로 이해하는데 매우 중요하다. 이전 연구에서는 주로 시각적 자극에 초점을 맞춰 이러한 관계를 탐구했다면, 본 연구는 음성으로 제시되는 감정맥락으로 탐구의 영역을 확장하여 언어적 맥락의 각성도와 정서가가 기억에 미치는 영향을 조사하고 특히 서열 위치 효과를 완화하는지에 주목하였다. 연구 1에서는 단어 목록의 중반부에 제시되는 음성 감정 맥락의 각성도가 단어 음성 기억에 어떤 영향을 주는지 알아보았다. 실험 결과, 각성도가 낮은 맥락에 노출되었을 때 목록 후반부의 기억력이 저하되는 것으로 나타났다. 연구 2에서는 각성도 수준을 통제했을 때 목록의 중반부에 제시되는 음성 감정 맥락의 정서가가 단어 음성 기억에 미치는 영향을 조사했다. 실험 결과, 부정적 맥락은 함께 제시되는 단어의 기억력을 저하시킨다는 사실을 발견했다. 연구 결과는 음성을 통해 제시되는 감정 맥락이 음성 자극의 기억 처리를 촉진하지 못한다는 것을 시사한다. 특히 부정적 정서 맥락은 서열 위치 효과를 강화하는 것으로 나타났다. 즉, 맥락과 기억 자극이 음성으로 동일할 경우 부정적 감정 맥락은 과제 수행에 간섭으로 작용하여 감정 맥락의 기억 향상 효과를 유발하지 못하는 경향을 보인다. 이는 청각적으로 전달되는 감정 맥락이 언어 기억 과정에 미치는 영향을 이해하는데 중요한 통찰을 제공한다.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제6권2호
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• pp.150-154
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• 2006

In this paper, we apply several pattern recognition algorithms to emotion recognition system with speech signal and compare the results. Firstly, we need emotional speech databases. Also, speech features for emotion recognition are determined on the database analysis step. Secondly, recognition algorithms are applied to these speech features. The algorithms we try are artificial neural network, Bayesian learning, Principal Component Analysis, LBG algorithm. Thereafter, the performance gap of these methods is presented on the experiment result section.

• 한국멀티미디어학회논문지
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• 제10권11호
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• pp.1530-1537
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• 2007

본 논문은 마이크로폰을 통해 실시간으로 습득된 음성으로부터 사람의 음성 감성상태를 평상, 기쁨, 슬픔, 화남 등 4가지로 구별할 수 있는 ARM 플랫폼 기반의 음성 감성인식 시스템 구현에 관한 것이다. 일반적으로 마이크로폰으로 수신된 음성은 화자 주변의 환경 잡음과 마이크로폰의 시스템 특성 때문에 입력 음성 신호가 왜곡되고 이로 인해 시스템의 성능이 저하된다. 본 논문에서는 이러한 잡음 영향을 최소화하기 위해 비교적 단순한 구조와 적은 연산 량을 가진 이동평균(MA, Moving Average) 필터를 입력 음성의 특징벡터 열에 적용하였다. 또한, 효율적으로 감성 특징벡터를 최적화할 수 있는 SFS(Sequential Forward Selection)기법을 적용해 제안 시스템의 성능을 최적화하였으며 감성 패턴 분류기로는 SVM(Support Vector Machine)을 사용하였다. 실험 결과 제안 감성인식 시스템은 모의실험에서 약 65%, ARM 플랫폼에서 약 62%의 인식률을 보였다.

• ETRI Journal
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• 제44권3호
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• pp.462-475
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• 2022

Although researchers have proposed numerous techniques for speech emotion recognition, its performance remains unsatisfactory in many application scenarios. In this study, we propose a speech emotion recognition model based on a genetic algorithm (GA)-decision tree (DT) fusion of deep and acoustic features. To more comprehensively express speech emotional information, first, frame-level deep and acoustic features are extracted from a speech signal. Next, five kinds of statistic variables of these features are calculated to obtain utterance-level features. The Fisher feature selection criterion is employed to select high-performance features, removing redundant information. In the feature fusion stage, the GA is is used to adaptively search for the best feature fusion weight. Finally, using the fused feature, the proposed speech emotion recognition model based on a DT support vector machine model is realized. Experimental results on the Berlin speech emotion database and the Chinese emotion speech database indicate that the proposed model outperforms an average weight fusion method.

• 제어로봇시스템학회논문지
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• 제12권3호
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• pp.284-288
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• 2006

In this paper, we apply several pattern recognition algorithms to emotion recognition system with speech signal and compare the results. Firstly, we need emotional speech databases. Also, speech features for emotion recognition is determined on the database analysis step. Secondly, recognition algorithms are applied to these speech features. The algorithms we try are artificial neural network, Bayesian learning, Principal Component Analysis, LBG algorithm. Thereafter, the performance gap of these methods is presented on the experiment result section. Truly, emotion recognition technique is not mature. That is, the emotion feature selection, relevant classification method selection, all these problems are disputable. So, we wish this paper to be a reference for the disputes.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 2006년도 춘계학술대회 학술발표 논문집 제16권 제1호
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• pp.347-350
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• 2006

In this paper, we apply several pattern recognition algorithms to emotion recognition system with speech signal and compare the results. Firstly, we need emotional speech databases. Also, speech features for emotion recognition is determined on the database analysis step. Secondly, recognition algorithms are applied to these speech features. The algorithms we try are artificial neural network, Bayesian learning, Principal Component Analysis, LBG algorithm. Thereafter, the performance gap of these methods is presented on the experiment result section. Truly, emotion recognition technique is not mature. That is, the emotion feature selection, relevant classification method selection, all these problems are disputable. So, we wish this paper to be a reference for the disputes.