Association between acoustic speech features and non-severe levels of anxiety and depression symptoms across lifespan

Background Several studies have investigated the acoustic effects of diagnosed anxiety and depression. Anxiety and depression are not characteristics of the typical aging process, but minimal or mild symptoms can appear and evolve with age. However, the knowledge about the association between speech and anxiety or depression is scarce for minimal/mild symptoms, typical of healthy aging. As longevity and aging are still a new phenomenon worldwide, posing also several clinical challenges, it is important to improve our understanding of non-severe mood symptoms’ impact on acoustic features across lifetime. The purpose of this study was to determine if variations in acoustic measures of voice are associated with non-severe anxiety or depression symptoms in adult population across lifetime. Methods Two different speech tasks (reading vowels in disyllabic words and describing a picture) were produced by 112 individuals aged 35-97. To assess anxiety and depression symptoms, the Hospital Anxiety Depression Scale (HADS) was used. The association between the segmental and suprasegmental acoustic parameters and HADS scores were analyzed using the linear multiple regression technique. Results The number of participants with presence of anxiety or depression symptoms is low (>7: 26.8% and 10.7%, respectively) and non-severe (HADS-A: 5.4 ± 2.9 and HADS-D: 4.2 ± 2.7, respectively). Adults with higher anxiety symptoms did not present significant relationships associated with the acoustic parameters studied. Adults with increased depressive symptoms presented higher vowel duration, longer total pause duration and short total speech duration. Finally, age presented a positive and significant effect only for depressive symptoms, showing that older participants tend to have more depressive symptoms. Conclusions Non-severe depression symptoms can be related to some acoustic parameters and age. Depression symptoms can be explained by acoustic parameters even among individuals without severe symptom levels.publishe

Detection of Verbal and Nonverbal speech features as markers of Depression: results of manual analysis and automatic classification

The present PhD project was the result of a multidisciplinary work involving psychiatrists, computing scientists, social signal processing experts and psychology students with the aim to analyse verbal and nonverbal behaviour in patients affected by Depression. Collaborations with several Clinical Health Centers were established for the collection of a group of patients suffering from depressive disorders. Moreover, a group of healthy controls was collected as well. A collaboration with the School of Computing Science of Glasgow University was established with the aim to analysed the collected data. Depression was selected for this study because is one of the most common mental disorder in the world (World Health Organization, 2017) associated with half of all suicides (Lecrubier, 2000). It requires prolonged and expensive medical treatments resulting into a significant burden for both patients and society (Olesen et al., 2012). The use of objective and reliable measurements of depressive symptoms can support the clinicians during the diagnosis reducing the risk of subjective biases and disorder misclassification (see discussion in Chapter 1) and doing the diagnosis in a quick and non-invasive way. Given this, the present PhD project proposes the investigation of verbal (i.e. speech content) and nonverbal (i.e. paralingiuistic features) behaviour in depressed patients to find several speech parameters that can be objective markers of depressive symptoms. The verbal and nonverbal behaviour are investigated through two kind of speech tasks: reading and spontaneous speech. Both manual features extraction and automatic classification approaches are used for this purpose. Differences between acute and remitted patients for prosodic and verbal features have been investigated as well. In addition, unlike other literature studies, in this project differences between subjects with and without Early Maladaptive Schema (EMS: Young et al., 2003) independently from the depressive symptoms, have been investigated with respect to both verbal and nonverbal behaviour. The proposed analysis shows that patients differ from healthy subjects for several verbal and nonverbal features. Moreover, using both reading and spontaneous speech, it is possible to automatically detect Depression with a good accuracy level (from 68 to 76%). These results demonstrate that the investigation of speech features can be a useful instrument, in addition to the current self-reports and clinical interviews, for helping the diagnosis of depressive disorders. Contrary to what was expected, patients in acute and remitted phase do not report differences regarding the nonverbal features and only few differences emerges for the verbal behaviour. At the same way, the automatic classification using paralinguistic features does not work well for the discrimination of subjects with and without EMS and only few differences between them have been found for the verbal behaviour. Possible explanations and limitations of these results will be discussed

Depresszió detektálása korrelációs struktúrán alkalmazott konvolúciós hálók segítségével

Jelen kutatásban a depressziós állapot automatikus detektálásának lehetőségét vizsgáltuk a beszédjelből kinyert speciális korrelációs struktúrán alkalmazott konvolúciós neurális hálok segítségével. A depresszió korunk egyik legelterjedtebb gyógyítható pszichiátriai betegsége. A depressziótól szenvedő egyén életminőségét nagymértékben befolyásolja a depresszió súlyossága, ami extrém esetben öngyilkossághoz is vezethet. Ezek alapján kulcsfontosságú, hogy már korai stádiumában felismerhető legyen a betegség és az illető megfelelő kezelésben részesüljön, azonban a depresszió diagnosztizálása szakértelmet kíván, emiatt fontos a depresszió esetleges jelenlétének automatikus jelzése. Ebben a cikkben egy olyan eljárást mutatunk be, ami beszédjel feldolgozása alapján tisztán spektrális jellemzőkön keresztül képes felismerni a depressziót konvolúciós neurális hálók alkalmazásának segítségével. Bemutatjuk, hogyan változik a depresszió detektálásának pontossága különböző akusztikai-fonetikai jellemzők felhasználása alapján, illetve a korrelációs struktúrának változtatása következtében. A módszer alkalmazásával 84%-os pontossággal tudtuk elkülöníteni az egészséges és depressziós személyeket a beszédmintáik alapján

주요 우울 장애의 음성 기반 분석: 연속적인 발화의 음향적 변화를 중심으로

학위논문(박사) -- 서울대학교대학원 : 융합과학기술대학원 융합과학부(디지털정보융합전공), 2023. 2. 이교구.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.주요 우울 장애 즉 흔히 우울증이라고 일컬어지는 기분 장애는 전 세계인 중 3.8%에 달하는 사람들이 겪은바 있는 매우 흔한 질병이다. 유전, 노화, 사회적 요인, 신경전달물질 체계의 이상등 다양한 원인으로 발생하는 우울증은 조기 발견 및 일상 생활에서의 관리가 매우 중요하다고 할 수 있다. 인간의 음성은 우울증을 관찰하기에 대표적인 바이오마커로 여겨져 왔으며, 음성 데이터를 기반으로한 자동 우울증 진단 시스템 개발을 위한 여러 연구들이 진행되어 왔다. 그러나 음성 말뭉치 구축의 어려움과 60세 이하의 성인들에게 초점이 맞추어진 연구, 정신과 의사들의 임상 소견을 바탕으로한 의학적 가설 설정의 미흡등의 한계점을 가지고 있으며, 이는 의료 진단 기구로 발전하는데 한계점이라고 할 수 있다. 또한, 항정신성 약물의 복용이 음성 특징에 미칠 수 있는 영향 또한 간과되고 있다. 본 논문에서는 위의 한계점들을 보완하기 위한 의미론적 수준 (문장 단위)에서의 음성 기반 자동 우울증 진단에 대한 연구를 시행하고자 한다. 우선적으로 감정의 변화가 음성 특징을 잘 반영되지 않는 노인층의 우울증 분석을 위해 감정 발화 문장을 개발하여 노인 우울증 음성 말뭉치를 구축하고, 문장 단위에서의 관찰을 통해 노인 우울증 군에서 감정 문장 발화가 미치는 영향과 감정 전이를 확인할 수 있었으며, 노인층의 자동 우울증 진단 시스템을 설계하였다. 최종적으로 항정신병 약물의 과복용으로 나타날 수 있는 대표적인 부작용인 추체외로 증상을 조사하기 위해 추체외로 증상 음성 말뭉치를 구축하였고, 항정신병 약물의 복용량과 음성 특징간의 상관관계를 분석하여 우울증의 치료 과정에서 항정신병 약물이 음성에 미칠 수 있는 영향에 대해서 조사하였다. 이를 통해 주요 우울 장애의 영역에 대한 포괄적인 연구를 진행하였다.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 초 록 121박

Speech-based automatic depression detection via biomarkers identification and artificial intelligence approaches

Depression has become one of the most prevalent mental health issues, affecting more than 300 million people all over the world. However, due to factors such as limited medical resources and accessibility to health care, there are still a large number of patients undiagnosed. In addition, the traditional approaches to depression diagnosis have limitations because they are usually time-consuming, and depend on clinical experience that varies across different clinicians. From this perspective, the use of automatic depression detection can make the diagnosis process much faster and more accessible. In this thesis, we present the possibility of using speech for automatic depression detection. This is based on the findings in neuroscience that depressed patients have abnormal cognition mechanisms thus leading to the speech differs from that of healthy people. Therefore, in this thesis, we show two ways of benefiting from automatic depression detection, i.e., identifying speech markers of depression and constructing novel deep learning models to improve detection accuracy. The identification of speech markers tries to capture measurable depression traces left in speech. From this perspective, speech markers such as speech duration, pauses and correlation matrices are proposed. Speech duration and pauses take speech fluency into account, while correlation matrices represent the relationship between acoustic features and aim at capturing psychomotor retardation in depressed patients. Experimental results demonstrate that these proposed markers are effective at improving the performance in recognizing depressed speakers. In addition, such markers show statistically significant differences between depressed patients and non-depressed individuals, which explains the possibility of using these markers for depression detection and further confirms that depression leaves detectable traces in speech. In addition to the above, we propose an attention mechanism, Multi-local Attention (MLA), to emphasize depression-relevant information locally. Then we analyse the effectiveness of MLA on performance and efficiency. According to the experimental results, such a model can significantly improve performance and confidence in the detection while reducing the time required for recognition. Furthermore, we propose Cross-Data Multilevel Attention (CDMA) to emphasize different types of depression-relevant information, i.e., specific to each type of speech and common to both, by using multiple attention mechanisms. Experimental results demonstrate that the proposed model is effective to integrate different types of depression-relevant information in speech, improving the performance significantly for depression detection

Envelhecimento vocal: estudo acústico-articulatório das alterações de fala com a idade

Background: Although the aging process causes specific alterations in the speech organs, the knowledge about the age effects in speech production is still disperse and incomplete. Objective: To provide a broader view of the age-related segmental and suprasegmental speech changes in European Portuguese (EP), considering new aspects besides static acoustic features, such as dynamic and articulatory data. Method: Two databases, with speech data of Portuguese adult native speakers obtained through standardized recording and segmentation procedures, were devised: i) an acoustic database containing all EP oral vowels produced in similar context (reading speech), and also a sample of semispontaneous speech (image description) collected from a large sample of adults between the ages 35 and 97; ii) and another with articulatory data (ultrasound (US) tongue images synchronized with speech) for all EP oral vowels produced in similar contexts (pseudowords and isolated) collected from young ([21-35]) and older ([55-73]) adults. Results: Based on the curated databases, various aspects of the aging speech were analyzed. Acoustically, the aging speech is characterized by: 1) longer vowels (in both genders); 2) a tendency for F0 to decrease in women and slightly increase in men; 3) lower vowel formant frequencies in females; 4) a significant reduction of the vowel acoustic space in men; 5) vowels with higher trajectory slope of F1 (in both genders); 6) shorter descriptions with higher pause time for males; 7) faster speech and articulation rate for females; and 8) lower HNR for females in semi-spontaneous speech. In addition, the total speech duration decrease is associated to non-severe depression symptoms and age. Older adults tended to present more depressive symptoms that could impact the amount of speech produced. Concerning the articulatory data, the tongue tends to be higher and more advanced with aging for almost all vowels, meaning that the vowel articulatory space tends to be higher, advanced, and bigger in older females. Conclusion: This study provides new information on aging speech for a language other than English. These results corroborate that speech changes with age and present different patterns between genders, and also suggest that speakers might develop specific articulatory adjustments with aging.Contextualização: Embora o processo de envelhecimento cause alterações específicas no sistema de produção de fala, o conhecimento sobre os efeitos da idade na fala é ainda disperso e incompleto. Objetivo: Proporcionar uma visão mais ampla das alterações segmentais e suprassegmentais da fala relacionadas com a idade no Português Europeu (PE), considerando outros aspetos, para além das características acústicas estáticas, tais como dados dinâmicos e articulatórios. Método: Foram criadas duas bases de dados, com dados de fala de adultos nativos do PE, obtidos através de procedimentos padronizados de gravação e segmentação: i) uma base de dados acústica contendo todas as vogais orais do PE em contexto semelhante (leitura de palavras), e também uma amostra de fala semiespontânea (descrição de imagem) produzidas por uma larga amostra de indivíduos entre os 35 e os 97 anos; ii) e outra com dados articulatórios (imagens de ultrassom da língua sincronizadas com o sinal acústico) de todas as vogais orais do PE produzidas em contextos semelhantes (pseudopalavras e palavras isoladas) por adultos de duas faixas etárias ([21-35] e [55-73]). Resultados: Tendo em conta as bases de dados curadas, foi analisado o efeito da idade em diversas características da fala. Acusticamente, a fala de pessoas mais velhas é caracterizada por: 1) vogais mais longas (ambos os sexos); 2) tendência para F0 diminuir nas mulheres e aumentar ligeiramente nos homens; 3) diminuição da frequência dos formantes das vogais nas mulheres; 4) redução significativa do espaço acústico das vogais nos homens; 5) vogais com maior inclinação da trajetória de F1 (ambos os sexos); 6) descrições mais curtas e com maior tempo de pausa nos homens; 7) aumento da velocidade articulatória e da velocidade de fala nas mulheres; e 8) diminuição do HNR na fala semiespontânea em mulheres. Além disso, os idosos tendem a apresentar mais sintomas depressivos que podem afetar a quantidade de fala produzida. Em relação aos dados articulatórios, a língua tende a apresentar-se mais alta e avançada em quase todas as vogais com a idade, ou seja o espaço articulatório das vogais tende a ser maior, mais alto e avançado nas mulheres mais velhas. Conclusão: Este estudo fornece novos dados sobre o efeito da idade na fala para uma língua diferente do inglês. Os resultados corroboram que a fala sofre alterações com a idade, que diferem em função do género, sugerindo ainda que os falantes podem desenvolver ajustes articulatórios específicos com a idade.Programa Doutoral em Gerontologia e Geriatri

Modern Views of Machine Learning for Precision Psychiatry

In light of the NIMH's Research Domain Criteria (RDoC), the advent of functional neuroimaging, novel technologies and methods provide new opportunities to develop precise and personalized prognosis and diagnosis of mental disorders. Machine learning (ML) and artificial intelligence (AI) technologies are playing an increasingly critical role in the new era of precision psychiatry. Combining ML/AI with neuromodulation technologies can potentially provide explainable solutions in clinical practice and effective therapeutic treatment. Advanced wearable and mobile technologies also call for the new role of ML/AI for digital phenotyping in mobile mental health. In this review, we provide a comprehensive review of the ML methodologies and applications by combining neuroimaging, neuromodulation, and advanced mobile technologies in psychiatry practice. Additionally, we review the role of ML in molecular phenotyping and cross-species biomarker identification in precision psychiatry. We further discuss explainable AI (XAI) and causality testing in a closed-human-in-the-loop manner, and highlight the ML potential in multimedia information extraction and multimodal data fusion. Finally, we discuss conceptual and practical challenges in precision psychiatry and highlight ML opportunities in future research

A toxicological survey of acute psychoses in Cape Coloured males with special reference to the cannabinoids

Many South African psychiatrists, and particularly those working in psychiatric hospitals with Black and Coloured patients, have the firm clinical impression that in many of these patients acute psychotic illness is associated with the abuse of cannabis. Most of the previous work in this field had been done by clinicians of Eastern countries where the use of cannabis has been endemic for thousands of years. However, those workers were handicapped because they lacked both the sophisticated techniques for standardized psychiatric evaluation and the availability of an assay to confirm cannabis use. It was decided to investigate acute psychoses in Cape Coloured males admitted to Valkenberg Hospital with the following aims: i. To identify a cohort of acutely psychotic patients who had recently been using cannabis and to compare them with a matched control group who were free of any drugs. The recently available EMITR immunochemical analytical technique was used for the detection of urinary cannabinoids. To exclude the contribution of other psychotropic agents to the aetiology of the psychoses, gas chromatography was performed to detect ethanol and thin-layer chromatography to screen for other psychotropic agents. ii. To assess the comprehensive mental state of patients on admission and then again after a 7-10 day period the Present State Examination (PSE), a well validated and standardized diagnostic instrument, was used. iii. To determine serum creatinine phosphokinase (CPK) and serum lactate dehydrogenase (LDH) levels (indicators of muscle damage) in view of the published reports of elevated levels in psychotic patients

Evaluation of Neuroprotective Effect of Celastrus Paniculatus on Cognition Impairment Caused by Phenytoin in Swiss Albino Mice

INTRODUCTION: Cognition refers to an individual’s thoughts, knowledge, interpretation, understanding and ideas himself and his environment. If the disturbances occur in these areas it leads to cognitive impairment. One of the established agents is Piracetam (PIM) which is also known for its anti-myoclonus activity and specific anti-amnesic activity in many experimental models is used for enhancing cognition. Loss of memory and cognitive function affects people worldwide, such loss may be the result of different progressive neurological disorders of the brain. It affects both men and women and is common in the elderly. OBJECTIVES: 1. To evaluate the cognition enhancement property of Celastrus paniculatus in phenytoin induced cognition impairment. 2. To determine the antiepileptic activity of Celastrus paniculatus. 3. To assess the hepatorenal toxicity of Celastrus paniculatus. MATERIAL AND METHODS: This study was conducted in Dhanalakshmi srinivasan medical college and hospital animal house, mices were separated in to twelve groups and was administered with drugs to specific groups and was evaluated with acute and chronic studies. Finally mices were sacrified and estimation of neurotransmitter was done and evaluation was done using uv visible spectrophotometer and fluorescence spectrofluorimeter. RESULTS: It is evaluated by behavior assessment of the mices using Radial arm maze apparatus test, Pole climbing apparatus test and increasing current electroshock seizures. Among the groups results obtained stated that P value was not significant in all groups but in GROUP II, III, IV phenytoin group, Acute and Chronic study was found to be significant in intra- groups. CONCLUSION: In this study the cognitive impairment was induced by phenytoin and the effect has been reversed by the standard drug piracetam and the experimental herbal (drug) Malkangani oil of celastrus paniculatus (CP). There is no difference between the two drugs and both exhibited similar efficacy