Behavioral sentiment analysis of depressive states

The need to release accurate and incontrovertible diagnoses of depression has fueled the search for new methodologies to obtain more reliable measurements than the commonly adopted questionnaires. In such a context, research has sought to identify non-biased measures derived from analyses of behavioral data such as voice and language. For this purpose, sentiment analysis techniques were developed, initially based on linguistic characteristics extracted from texts and gradually becoming more and more sophisticated by adding tools for the analyses of voice and visual data (such as facial expressions and movements). This work summarizes the behavioral features accounted for detecting depressive states and sentiment analysis tools developed to extract them from text, audio, and video recordings

VOCAL BIOMARKERS OF CLINICAL DEPRESSION: WORKING TOWARDS AN INTEGRATED MODEL OF DEPRESSION AND SPEECH

Speech output has long been considered a sensitive marker of a person’s mental state. It has been previously examined as a possible biomarker for diagnosis and treatment response for certain mental health conditions, including clinical depression. To date, it has been difficult to draw robust conclusions from past results due to diversity in samples, speech material, investigated parameters, and analytical methods. Within this exploratory study of speech in clinically depressed individuals, articulatory and phonatory behaviours are examined in relation to psychomotor symptom profiles and overall symptom severity. A systematic review provided context from the existing body of knowledge on the effects of depression on speech, and provided context for experimental setup within this body of work. Examinations of vowel space, monophthong, and diphthong productions as well as a multivariate acoustic analysis of other speech parameters (e.g., F0 range, perturbation measures, composite measures, etc.) are undertaken with the goal of creating a working model of the effects of depression on speech. Initial results demonstrate that overall vowel space area was not different between depressed and healthy speakers, but on closer inspection, this was due to more specific deficits seen in depressed patients along the first formant (F1) axis. Speakers with depression were more likely to produce centralised vowels along F1, as compared to F2—and this was more pronounced for low-front vowels, which are more complex given the degree of tongue-jaw coupling required for production. This pattern was seen in both monophthong and diphthong productions. Other articulatory and phonatory measures were inspected in a factor analysis as well, suggesting additional vocal biomarkers for consideration in diagnosis and treatment assessment of depression—including aperiodicity measures (e.g., higher shimmer and jitter), changes in spectral slope and tilt, and additive noise measures such as increased harmonics-to-noise ratio. Intonation was also affected by diagnostic status, but only for specific speech tasks. These results suggest that laryngeal and articulatory control is reduced by depression. Findings support the clinical utility of combining Ellgring and Scherer’s (1996) psychomotor retardation and social-emotional hypotheses to explain the effects of depression on speech, which suggest observed changes are due to a combination of cognitive, psycho-physiological and motoric mechanisms. Ultimately, depressive speech is able to be modelled along a continuum of hypo- to hyper-speech, where depressed individuals are able to assess communicative situations, assess speech requirements, and then engage in the minimum amount of motoric output necessary to convey their message. As speakers fluctuate with depressive symptoms throughout the course of their disorder, they move along the hypo-hyper-speech continuum and their speech is impacted accordingly. Recommendations for future clinical investigations of the effects of depression on speech are also presented, including suggestions for recording and reporting standards. Results contribute towards cross-disciplinary research into speech analysis between the fields of psychiatry, computer science, and speech science

Intelligent Advanced User Interfaces for Monitoring Mental Health Wellbeing

It has become pressing to develop objective and automatic measurements integrated in intelligent diagnostic tools for detecting and monitoring depressive states and enabling an increased precision of diagnoses and clinical decision-makings. The challenge is to exploit behavioral and physiological biomarkers and develop Artificial Intelligent (AI) models able to extract information from a complex combination of signals considered key symptoms. The proposed AI models should be able to help clinicians to rapidly formulate accurate diagnoses and suggest personalized intervention plans ranging from coaching activities (exploiting for example serious games), support networks (via chats, or social networks), and alerts to caregivers, doctors, and care control centers, reducing the considerable burden on national health care institutions in terms of medical, and social costs associated to depression cares

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

Models and Analysis of Vocal Emissions for Biomedical Applications

The International Workshop on Models and Analysis of Vocal Emissions for Biomedical Applications (MAVEBA) came into being in 1999 from the particularly felt need of sharing know-how, objectives and results between areas that until then seemed quite distinct such as bioengineering, medicine and singing. MAVEBA deals with all aspects concerning the study of the human voice with applications ranging from the newborn to the adult and elderly. Over the years the initial issues have grown and spread also in other fields of research such as occupational voice disorders, neurology, rehabilitation, image and video analysis. MAVEBA takes place every two years in Firenze, Italy. This edition celebrates twenty-two years of uninterrupted and successful research in the field of voice analysis

The Production of Emotional Prosdy in Varying Severities of Apraxia of Speech

One mild AOS, one moderate AOS and one control speaker were asked to produce utterances with different emotional intent. In Experiment 1, the three subjects were asked to produce sentences with a happy, sad, or neutral intent through a repetition task. In Experiment 2, the three subjects were asked to produce sentences with either a happy or sad intent through a picture elicitation task. Paired t-tests comparing data from the acoustic analyses of each subject\u27s utterances revealed significant differences between FO, duration, and intensity characteristics between the happy and sad sentences of the control speaker. There were no significant differences in the acoustic characteristics of the productions of the AOS speakers suggesting that the AOS subjects were unable to volitionally produce acoustic parameters that help convey emotion. Two more experiments were designed to determine if näive listeners could hear the acoustic cues to signal emotion in all three speakers. In Experiment 3, näive listeners were asked to identify the sentences produced in Experiment 1 as happy, sad, or neutral. In Experiment 4, näive listeners were asked to identify the sentences produced in Experiment 2 as either happy or sad. Chi-square findings revealed that the naive listeners were able to identify the emotional differences of the control speaker and the correct identification was not by chance. The näive listeners could not distinguish between the emotional utterances of the mild or moderate AOS speakers. Higher percentages of correct identification in certain sentences over others were artifacts attributed to either chance (the näive listeners were guessing) or a response strategy (when in doubt, the naive listeners chose neutral or sad). The findings from Exp. 3 & 4 corroborate the acoustic findings from Exp. 1 & 2. In addition to the 4 structured experiments, spontaneous samples of happy, sad, and neutral utterances were collected and compared to those sentences produced in Experiments 1 & 2. Comparisons between the elicited and spontaneous sentences indicated that the moderate AOS subject was able to produce variations of FO and duration similar to those variations that would be produced by normal speakers conveying emotion (Banse & Scherer, 1996; Lieberman & Michaels, 1962; Scherer, 1988). The mild AOS subject was unable to produce prosodic differences between happy and sad emotion. This study found that although these AOS subjects were unable to produce acoustic parameters during elicited speech that signal emotion, they were able to produce some more variation in the acoustic properties of FO and duration, especially in the moderate AOS speaker. However, any meaningful variation pattern that would convey emotion (such as seen in the control subject) were not found. These findings suggest that the AOS subjects probably convey emotion non-verbally (e.g., facial expression, muscle tension, body language)

Models and Analysis of Vocal Emissions for Biomedical Applications

The Models and Analysis of Vocal Emissions with Biomedical Applications (MAVEBA) workshop came into being in 1999 from the particularly felt need of sharing know-how, objectives and results between areas that until then seemed quite distinct such as bioengineering, medicine and singing. MAVEBA deals with all aspects concerning the study of the human voice with applications ranging from the neonate to the adult and elderly. Over the years the initial issues have grown and spread also in other aspects of research such as occupational voice disorders, neurology, rehabilitation, image and video analysis. MAVEBA takes place every two years always in Firenze, Italy

Models and analysis of vocal emissions for biomedical applications: 5th International Workshop: December 13-15, 2007, Firenze, Italy

The MAVEBA Workshop proceedings, held on a biannual basis, collect the scientific papers presented both as oral and poster contributions, during the conference. The main subjects are: development of theoretical and mechanical models as an aid to the study of main phonatory dysfunctions, as well as the biomedical engineering methods for the analysis of voice signals and images, as a support to clinical diagnosis and classification of vocal pathologies. The Workshop has the sponsorship of: Ente Cassa Risparmio di Firenze, COST Action 2103, Biomedical Signal Processing and Control Journal (Elsevier Eds.), IEEE Biomedical Engineering Soc. Special Issues of International Journals have been, and will be, published, collecting selected papers from the conference