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

DeepTMH: Multimodal Semi-supervised framework leveraging Affective and Cognitive engagement for Telemental Health

To aid existing telemental health services, we propose DeepTMH, a novel framework that models telemental health session videos by extracting latent vectors corresponding to Affective and Cognitive features frequently used in psychology literature. Our approach leverages advances in semi-supervised learning to tackle the data scarcity in the telemental health session video domain and consists of a multimodal semi-supervised GAN to detect important mental health indicators during telemental health sessions. We demonstrate the usefulness of our framework and contrast against existing works in two tasks: Engagement regression and Valence-Arousal regression, both of which are important to psychologists during a telemental health session. Our framework reports 40% improvement in RMSE over SOTA method in Engagement Regression and 50% improvement in RMSE over SOTA method in Valence-Arousal Regression. To tackle the scarcity of publicly available datasets in telemental health space, we release a new dataset, MEDICA, for mental health patient engagement detection. Our dataset, MEDICA consists of 1299 videos, each 3 seconds long. To the best of our knowledge, our approach is the first method to model telemental health session data based on psychology-driven Affective and Cognitive features, which also accounts for data sparsity by leveraging a semi-supervised setup

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

Stress and emotion recognition in natural speech in the work and family environments

The speech stress and emotion recognition and classification technology has a potential to provide significant benefits to the national and international industry and society in general. The accuracy of an automatic emotion speech and emotion recognition relays heavily on the discrimination power of the characteristic features. This work introduced and examined a number of new linear and nonlinear feature extraction methods for an automatic detection of stress and emotion in speech. The proposed linear feature extraction methods included features derived from the speech spectrograms (SS-CB/BARK/ERB-AE, SS-AF-CB/BARK/ERB-AE, SS-LGF-OFS, SS-ALGF-OFS, SS-SP-ALGF-OFS and SS-sigma-pi), wavelet packets (WP-ALGF-OFS) and the empirical mode decomposition (EMD-AER). The proposed nonlinear feature extraction methods were based on the results of recent laryngological studies and nonlinear modelling of the phonation process. The proposed nonlinear features included the area under the TEO autocorrelation envelope based on different spectral decompositions (TEO-DWT, TEO-WP, TEO-PWP-S and TEO-PWP-G), as well as features representing spectral energy distribution of speech (AUSEES) and glottal waveform (AUSEEG). The proposed features were compared with features based on the classical linear model of speech production including F0, formants, MFCC and glottal time/frequency parameters. Two classifiers GMM and KNN were tested for consistency. The experiments used speech under actual stress from the SUSAS database (7 speakers; 3 female and 4 male) and speech with five naturally expressed emotions (neutral, anger, anxious, dysphoric and happy) from the ORI corpora (71 speakers; 27 female and 44 male). The nonlinear features clearly outperformed all the linear features. The classification results demonstrated consistency with the nonlinear model of the phonation process indicating that the harmonic structure and the spectral distribution of the glottal energy provide the most important cues for stress and emotion recognition in speech. The study also investigated if the automatic emotion recognition can determine differences in emotion expression between parents of depressed adolescents and parents of non-depressed adolescents. It was also investigated if there are differences in emotion expression between mothers and fathers in general. The experiment results indicated that parents of depressed adolescent produce stronger more exaggerated expressions of affect than parents of non-depressed children. And females in general provide easier to discriminate (more exaggerated) expressions of affect than males

Models and analysis of vocal emissions for biomedical applications

This book of Proceedings collects the papers presented at the 3rd International Workshop on Models and Analysis of Vocal Emissions for Biomedical Applications, MAVEBA 2003, held 10-12 December 2003, Firenze, Italy. The workshop is organised every two years, and aims to stimulate contacts between specialists active in research and industrial developments, in the area of voice analysis for biomedical applications. The scope of the Workshop includes all aspects of voice modelling and analysis, ranging from fundamental research to all kinds of biomedical applications and related established and advanced technologies

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

Analysis and detection of human emotion and stress from speech signals

Ph.DDOCTOR OF PHILOSOPH

Exposing the hidden vocal channel: Analysis of vocal expression

This dissertation explored perception and modeling of human vocal expression, and began by asking what people heard in expressive speech. To address this fundamental question, clips from Shakespearian soliloquy and from the Library of Congress Veterans Oral History Collection were presented to Mechanical Turk workers (10 per clip); and the workers were asked to provide 1-3 keywords describing the vocal expression in the voice. The resulting keywords described prosody, voice quality, nonverbal quality, and emotion in the voice, along with the conversational style, and personal qualities attributed to the speaker. More than half of the keywords described emotion, and were wide-ranging and nuanced. In contrast, keywords describing prosody and voice quality reduced to a short list of frequently-repeating vocal elements. Given this description of perceived vocal expression, a 3-step process was used to model vocal qualities which listeners most frequently perceived. This process included 1) an interactive analysis across each condition to discover its distinguishing characteristics, 2) feature selection and evaluation via unequal variance sensitivity measurements and examination of means and 2-sigma variances across conditions, and 3) iterative, incremental classifier training and validation. The resulting models performed at 2-3.5 times chance. More importantly, the analysis revealed a continuum relationship across whispering, breathiness, modal speech, and resonance, and revealed multiple spectral sub-types of breathiness, modal speech, resonance, and creaky voice. Finally, latent semantic analysis (LSA) applied to the crowdsourced keyword descriptors enabled organic discovery of expressive dimensions present in each corpus, and revealed relationships among perceived voice qualities and emotions within each dimension and across the corpora. The resulting dimensional classifiers performed at up to 3 times chance, and a second study presented a dimensional analysis of laughter. This research produced a new way of exploring emotion in the voice, and of examining relationships among emotion, prosody, voice quality, conversation quality, personal quality, and other expressive vocal elements. For future work, this perception-grounded fusion of crowdsourcing and LSA technique can be applied to anything humans can describe, in any research domain