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์ฃผ์ ์ฐ์ธ ์ฅ์ ์ ์์ฑ ๊ธฐ๋ฐ ๋ถ์: ์ฐ์์ ์ธ ๋ฐํ์ ์ํฅ์ ๋ณํ๋ฅผ ์ค์ฌ์ผ๋ก
ํ์๋
ผ๋ฌธ(๋ฐ์ฌ) -- ์์ธ๋ํ๊ต๋ํ์ : ์ตํฉ๊ณผํ๊ธฐ์ ๋ํ์ ์ตํฉ๊ณผํ๋ถ(๋์งํธ์ ๋ณด์ตํฉ์ ๊ณต), 2023. 2. ์ด๊ต๊ตฌ.Major depressive disorder (commonly referred to as depression) is a common disorder that affects 3.8% of the world's population. Depression stems from various causes, such as genetics, aging, social factors, and abnormalities in the neurotransmitter system; thus, early detection and monitoring are essential. The human voice is considered a representative biomarker for observing depression; accordingly, several studies have developed an automatic depression diagnosis system based on speech.
However, constructing a speech corpus is a challenge, studies focus on adults under 60 years of age, and there are insufficient medical hypotheses based on the clinical findings of psychiatrists, limiting the evolution of the medical diagnostic tool. Moreover, the effect of taking antipsychotic drugs on speech characteristics during the treatment phase is overlooked.
Thus, this thesis studies a speech-based automatic depression diagnosis system at the semantic level (sentence). First, to analyze depression among the elderly whose emotional changes do not adequately reflect speech characteristics, it developed the mood-induced sentence to build the elderly depression speech corpus and designed an automatic depression diagnosis system for the elderly.
Second, it constructed an extrapyramidal symptom speech corpus to investigate the extrapyramidal symptoms, a typical side effect that can appear from an antipsychotic drug overdose. Accordingly, there is a strong correlation between the antipsychotic dose and speech characteristics. The study paved the way for a comprehensive examination of the automatic diagnosis system for depression.์ฃผ์ ์ฐ์ธ ์ฅ์ ์ฆ ํํ ์ฐ์ธ์ฆ์ด๋ผ๊ณ ์ผ์ปฌ์ด์ง๋ ๊ธฐ๋ถ ์ฅ์ ๋ ์ ์ธ๊ณ์ธ ์ค 3.8%์ ๋ฌํ๋ ์ฌ๋๋ค์ด ๊ฒช์๋ฐ ์๋ ๋งค์ฐ ํํ ์ง๋ณ์ด๋ค. ์ ์ , ๋
ธํ, ์ฌํ์ ์์ธ, ์ ๊ฒฝ์ ๋ฌ๋ฌผ์ง ์ฒด๊ณ์ ์ด์๋ฑ ๋ค์ํ ์์ธ์ผ๋ก ๋ฐ์ํ๋ ์ฐ์ธ์ฆ์ ์กฐ๊ธฐ ๋ฐ๊ฒฌ ๋ฐ ์ผ์ ์ํ์์์ ๊ด๋ฆฌ๊ฐ ๋งค์ฐ ์ค์ํ๋ค๊ณ ํ ์ ์๋ค. ์ธ๊ฐ์ ์์ฑ์ ์ฐ์ธ์ฆ์ ๊ด์ฐฐํ๊ธฐ์ ๋ํ์ ์ธ ๋ฐ์ด์ค๋ง์ปค๋ก ์ฌ๊ฒจ์ ธ ์์ผ๋ฉฐ, ์์ฑ ๋ฐ์ดํฐ๋ฅผ ๊ธฐ๋ฐ์ผ๋กํ ์๋ ์ฐ์ธ์ฆ ์ง๋จ ์์คํ
๊ฐ๋ฐ์ ์ํ ์ฌ๋ฌ ์ฐ๊ตฌ๋ค์ด ์งํ๋์ด ์๋ค. ๊ทธ๋ฌ๋ ์์ฑ ๋ง๋ญ์น ๊ตฌ์ถ์ ์ด๋ ค์๊ณผ 60์ธ ์ดํ์ ์ฑ์ธ๋ค์๊ฒ ์ด์ ์ด ๋ง์ถ์ด์ง ์ฐ๊ตฌ, ์ ์ ๊ณผ ์์ฌ๋ค์ ์์ ์๊ฒฌ์ ๋ฐํ์ผ๋กํ ์ํ์ ๊ฐ์ค ์ค์ ์ ๋ฏธํก๋ฑ์ ํ๊ณ์ ์ ๊ฐ์ง๊ณ ์์ผ๋ฉฐ, ์ด๋ ์๋ฃ ์ง๋จ ๊ธฐ๊ตฌ๋ก ๋ฐ์ ํ๋๋ฐ ํ๊ณ์ ์ด๋ผ๊ณ ํ ์ ์๋ค. ๋ํ, ํญ์ ์ ์ฑ ์ฝ๋ฌผ์ ๋ณต์ฉ์ด ์์ฑ ํน์ง์ ๋ฏธ์น ์ ์๋ ์ํฅ ๋ํ ๊ฐ๊ณผ๋๊ณ ์๋ค.
๋ณธ ๋
ผ๋ฌธ์์๋ ์์ ํ๊ณ์ ๋ค์ ๋ณด์ํ๊ธฐ ์ํ ์๋ฏธ๋ก ์ ์์ค (๋ฌธ์ฅ ๋จ์)์์์ ์์ฑ ๊ธฐ๋ฐ ์๋ ์ฐ์ธ์ฆ ์ง๋จ์ ๋ํ ์ฐ๊ตฌ๋ฅผ ์ํํ๊ณ ์ ํ๋ค. ์ฐ์ ์ ์ผ๋ก ๊ฐ์ ์ ๋ณํ๊ฐ ์์ฑ ํน์ง์ ์ ๋ฐ์๋์ง ์๋ ๋
ธ์ธ์ธต์ ์ฐ์ธ์ฆ ๋ถ์์ ์ํด ๊ฐ์ ๋ฐํ ๋ฌธ์ฅ์ ๊ฐ๋ฐํ์ฌ ๋
ธ์ธ ์ฐ์ธ์ฆ ์์ฑ ๋ง๋ญ์น๋ฅผ ๊ตฌ์ถํ๊ณ , ๋ฌธ์ฅ ๋จ์์์์ ๊ด์ฐฐ์ ํตํด ๋
ธ์ธ ์ฐ์ธ์ฆ ๊ตฐ์์ ๊ฐ์ ๋ฌธ์ฅ ๋ฐํ๊ฐ ๋ฏธ์น๋ ์ํฅ๊ณผ ๊ฐ์ ์ ์ด๋ฅผ ํ์ธํ ์ ์์์ผ๋ฉฐ, ๋
ธ์ธ์ธต์ ์๋ ์ฐ์ธ์ฆ ์ง๋จ ์์คํ
์ ์ค๊ณํ์๋ค. ์ต์ข
์ ์ผ๋ก ํญ์ ์ ๋ณ ์ฝ๋ฌผ์ ๊ณผ๋ณต์ฉ์ผ๋ก ๋ํ๋ ์ ์๋ ๋ํ์ ์ธ ๋ถ์์ฉ์ธ ์ถ์ฒด์ธ๋ก ์ฆ์์ ์กฐ์ฌํ๊ธฐ ์ํด ์ถ์ฒด์ธ๋ก ์ฆ์ ์์ฑ ๋ง๋ญ์น๋ฅผ ๊ตฌ์ถํ์๊ณ , ํญ์ ์ ๋ณ ์ฝ๋ฌผ์ ๋ณต์ฉ๋๊ณผ ์์ฑ ํน์ง๊ฐ์ ์๊ด๊ด๊ณ๋ฅผ ๋ถ์ํ์ฌ ์ฐ์ธ์ฆ์ ์น๋ฃ ๊ณผ์ ์์ ํญ์ ์ ๋ณ ์ฝ๋ฌผ์ด ์์ฑ์ ๋ฏธ์น ์ ์๋ ์ํฅ์ ๋ํด์ ์กฐ์ฌํ์๋ค. ์ด๋ฅผ ํตํด ์ฃผ์ ์ฐ์ธ ์ฅ์ ์ ์์ญ์ ๋ํ ํฌ๊ด์ ์ธ ์ฐ๊ตฌ๋ฅผ ์งํํ์๋ค.Chapter 1 Introduction 1
1.1 Research Motivations 3
1.1.1 Bridging the Gap Between Clinical View and Engineering 3
1.1.2 Limitations of Conventional Depressed Speech Corpora 4
1.1.3 Lack of Studies on Depression Among the Elderly 4
1.1.4 Depression Analysis on Semantic Level 6
1.1.5 How Antipsychotic Drug Affects the Human Voice? 7
1.2 Thesis objectives 9
1.3 Outline of the thesis 10
Chapter 2 Theoretical Background 13
2.1 Clinical View of Major Depressive Disorder 13
2.1.1 Types of Depression 14
2.1.2 Major Causes of Depression 15
2.1.3 Symptoms of Depression 17
2.1.4 Diagnosis of Depression 17
2.2 Objective Diagnostic Markers of Depression 19
2.3 Speech in Mental Disorder 19
2.4 Speech Production and Depression 21
2.5 Automatic Depression Diagnostic System 23
2.5.1 Acoustic Feature Representation 24
2.5.2 Classification / Prediction 27
Chapter 3 Developing Sentences for New Depressed Speech Corpus 31
3.1 Introduction 31
3.2 Building Depressed Speech Corpus 32
3.2.1 Elements of Speech Corpus Production 32
3.2.2 Conventional Depressed Speech Corpora 35
3.2.3 Factors Affecting Depressed Speech Characteristics 39
3.3 Motivations 40
3.3.1 Limitations of Conventional Depressed Speech Corpora 40
3.3.2 Attitude of Subjects to Depression: Masked Depression 43
3.3.3 Emotions in Reading 45
3.3.4 Objectives of this Chapter 45
3.4 Proposed Methods 46
3.4.1 Selection of Words 46
3.4.2 Structure of Sentence 47
3.5 Results 49
3.5.1 Mood-Inducing Sentences (MIS) 49
3.5.2 Neutral Sentences for Extrapyramidal Symptom Analysis 49
3.6 Summary 51
Chapter 4 Screening Depression in The Elderly 52
4.1 Introduction 52
4.2 Korean Elderly Depressive Speech Corpus 55
4.2.1 Participants 55
4.2.2 Recording Procedure 57
4.2.3 Recording Specification 58
4.3 Proposed Methods 59
4.3.1 Voice-based Screening Algorithm for Depression 59
4.3.2 Extraction of Acoustic Features 59
4.3.3 Feature Selection System and Distance Computation 62
4.3.4 Classification and Statistical Analyses 63
4.4 Results 65
4.5 Discussion 69
4.6 Summary 74
Chapter 5 Correlation Analysis of Antipsychotic Dose and Speech Characteristics 75
5.1 Introduction 75
5.2 Korean Extrapyramidal Symptoms Speech Corpus 78
5.2.1 Participants 78
5.2.2 Recording Process 79
5.2.3 Extrapyramidal Symptoms Annotation and Equivalent Dose Calculations 80
5.3 Proposed Methods 81
5.3.1 Acoustic Feature Extraction 81
5.3.2 Speech Characteristics Analysis recording to Eq.dose 83
5.4 Results 83
5.5 Discussion 87
5.6 Summary 90
Chapter 6 Conclusions and Future Work 91
6.1 Conclusions 91
6.2 Future work 95
Bibliography 97
์ด ๋ก 121๋ฐ
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Recent research on depression suggests that speech can reveal underlying processes in the mind of the depressed. This paper systematically reviews the literature on linguistic features in depression. A corpus of 26 papers investigating the relation between depression and one of the three linguistic features, first-person singular pronouns, positive emotion words, or negative emotion words, were analysed. Three meta-analyses were performed on the three linguistic features. The meta-analyses identify differences in first-person singular pronoun use, negative emotion word use, and positive emotion word use between depressed individuals and healthy controls (Cohenโs d of 0.44, 0.72 and -0.38). Furthermore, the meta-analyses identify correlations for severity of depression and first-person singular pronoun use, negative emotion word use, and positive emotion word use (Pearsonโs r of 0.19, 0.12 and -0.21). All three linguistic features produced small to medium effect sizes thus suggesting a relation between the use of the linguistic features and depression. The effect was not moderated by age or type of task the respondents completed.Recent research on depression suggests that speech can reveal underlying processes in the mind of the depressed. This paper systematically reviews the literature on linguistic features in depression. A corpus of 26 papers investigating the relation between depression and one of the three linguistic features, first-person singular pronouns, positive emotion words, or negative emotion words, were analysed. Three meta-analyses were performed on the three linguistic features. The meta-analyses identify differences in first-person singular pronoun use, negative emotion word use, and positive emotion word use between depressed individuals and healthy controls (Cohenโs d of 0.44, 0.72 and -0.38). Furthermore, the meta-analyses identify correlations for severity of depression and first-person singular pronoun use, negative emotion word use, and positive emotion word use (Pearsonโs r of 0.19, 0.12 and -0.21). All three linguistic features produced small to medium effect sizes thus suggesting a relation between the use of the linguistic features and depression. The effect was not moderated by age or type of task the respondents completed
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