The motivation for this work is in an attempt to rectify the current lack of objective tools for clinical analysis of emotional disorders. This study involves the examination of a large breadth of objectively measurable features for use in discriminating depressed speech. Analysis is based on features related to prosodics, the vocal tract, and parameters extracted directly from the glottal waveform. Discrimination of the depressed speech was based on a feature selection strategy utilizing the following combinations of feature domains: prosodic measures alone, prosodic and vocal tract measures, prosodic and glottal measures, and all three domains. The combination of glottal and prosodic features produced better discrimination overall than the combination of prosodic and vocal tract features. Analysis of discriminating feature sets used in the study reflect a clear indication that glottal descriptors are vital components of vocal affect analysis.

Human communication is saturated with emotional context that aids in interpreting a speakers mental state. Speech analysis research involving the classification of emotional states has been studied primarily with prosodic (e.g., pitch, energy, speaking rate) and/or spectral (e.g., formants) features. Glottal waveform features, while receiving less attention (due primarily to the difficulty of feature extraction), have also shown strong clustering potential of various emotional and stress states. This study provides a comparison of the major categories of speech analysis in the application of identifying and clustering feature statistics from a control group and a patient group suffering from a clinical diagnosis of depression.

Background Assessment and diagnosis of post-stroke depression (PSD) among patients with aphasia presents unique challenges. A gold-standard assessment of PSD among this population has yet to be identified. Objectives The first aim was to investigate the association between two depression scales developed for assessing depressive symptoms among patients with aphasia. The second aim was to evaluate the relation between these scales and a measure of perceived stress. Method Twenty-five (16 male; 9 female) individuals with history of left-hemisphere cerebrovascular accident (CVA) were assessed for depression and perceived stress using the Stroke Aphasic Depression Questionnaire-10 (SADQ-10), the Aphasia Depression Rating Scale (ADRS), and the Perceived Stress Scale (PSS). Results SADQ-10 and ADRS ratings were strongly correlated with each other (r = 0.708, p < 0.001). SADQ-10 ratings were strongly correlated with PSS ratings (r = 0.620, p = 0.003), while ADRS ratings were moderately correlated (r = 0.492, p = 0.027). Item analysis of each scale identified items which increased both inter-scale correlation and intra-scale consistency when excluded. Conclusions The SADQ-10 and ADRS appear to be acceptable measures of depressive symptoms in aphasia patients. Measurements of perceived stress may also be an important factor in assessment of depressive symptoms.

The threat of obesity, diabetes, anorexia, and bulimia in our society today has motivated extensive research on dietary monitoring. Standard self-report methods such as 24-h recall and food frequency questionnaires are expensive, burdensome, and unreliable to handle the growing health crisis. Long-term activity monitoring in daily living is a promising approach to provide individuals with quantitative feedback that can encourage healthier habits. Although several studies have attempted automating dietary monitoring using wearable, handheld, smart-object, and environmental systems, it remains an open research problem. This paper aims to provide a comprehensive review of wearable and hand-held approaches from 2004 to 2016. Emphasis is placed on sensor types used, signal analysis and machine learning methods, as well as a benchmark of state-of-the art work in this field. Key issues, challenges, and gaps are highlighted to motivate future work toward development of effective, reliable, and robust dietary monitoring systems.

No abstract