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

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 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. This edition celebrates twenty years of uninterrupted and succesfully research in the field of voice analysis

Models and Analysis of Vocal Emissions for Biomedical Applications

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

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

Reconhecimento de patologias da voz usando técnicas de processamento da fala

O diagnóstico de patologias da voz envolve normalmente métodos invasivos que requerem esforços conjuntos de equipas multidisciplinares. A utilização de um método automático baseado em técnicas de processamento de fala, sendo não invasivo e rápido, pode ser um método de rastreio ou um diagnóstico preliminar ao realizado por especialistas. Esta tese propõe soluções para a identificação de patologias da voz através do processamento do sinal de fala. Os métodos utilizados envolvem classificadores tipicamente usados em reconhecimento de orador, como por exemplo support vector machines e Gaussian mixture models. Os parâmetros que modelam a fonte do aparelho fonador não têm obtido resultados relevantes na distinção entre patologias. Contudo abordagens com parâmetros que modelam o trato vocal obtêm melhor sucesso nesta tarefa, assim como nos diagnósticos de vozes patológicas. Nesta linha, os parâmetros utilizados nesses classificadores têm como objectivo modelar o trato vocal, como por exemplo os mel-frequency cepstral coefficients, os line spectral frequencies e mel-line spectral frequencies. É ainda proposto o uso de fala contínua como sinal para a identificação de patologias. Este sinal, ao exigir um maior esforço por parte do paciente e por ser mais rico em termos fonéticos, aliado ao facto de as patologias da voz produzirem alteração em todos os fonemas, permite melhorar os resultados no diagnóstico. Nesta abordagem foram realizados testes usando três classes: sujeitos saudáveis; sujeitos com patologias laríngeas fisiológicas (edemas e nódulos); e sujeitos com patologias laríngeas neuromusculares (paralisia unilateral das pregas vocais). A taxa de reconhecimento obtida foi de 84% para as três classes. Esta tese propõe também soluções para o reconhecimento de vozes patológicas, com base na análise de formantes e na relação harmónicas-ruído. Neste sentido, foi efectuada a implementação de um algoritmo simples baseado em árvores de decisão que permitiu uma taxa de reconhecimento de 95%