Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations.
MFCC-based Recurrent Neural Network for Automatic Clinical Depression Recognition and Assessment from Speech Emna Rejaibi a,b,c, Ali Komaty d, Fabrice Meriaudeau e, Said Agrebi c, Alice Othmani a a Universit e Paris-Est, LISSI, UPEC, 94400 Vitry sur Seine, France b INSAT Institut National des Sciences Appliqu ees et de T echnologie, Centre Urbain Nord BP 676-1080, Tunis, Tunisie c Y obitrust, T echnopark El Gazala B11 Route de Raoued Km 3.5, 2088 Ariana, Tunisie d University of Sciences and Arts in Lebanon, Ghobeiry, Liban e Universit e de Bourgogne Franche Comt e, ImvIA EA7535/ IFTIM Abstract Major depression, also known as clinical depression, is a constant sense of despair and hopelessness. It is a major mental disorder that can a ff ect people of any age including children and that a ff ect negatively person's personal life, work life, social life and health conditions. Globally, over 300 million people of all ages are estimated to su ff er from clinical depression. A deep recurrent neural network-based framework is presented in this paper to detect depression and to predict its severity level from speech. Low-level and high-level audio features are extracted from audio recordings to predict the 24 scores of the Patient Health Questionnaire (a depression assessment test) and the binary class of depression diagnosis. To overcome the problem of the small size of Speech Depression Recognition (SDR) datasets, data augmentation techniques are used to expand the labeled training set and also transfer learning is performed where the proposed model is trained on a related task and reused as starting point for the proposed model on SDR task. The proposed framework is evaluated on the DAIC-WOZ corpus of the A VEC2017 challenge and promising results are obtained. An overall accuracy of 76.27% with a root mean square error of 0.4 is achieved in assessing depression, while a root mean square error of 0.168 is achieved in predicting the depression severity levels. Introduction Depression is a mental disorder caused by several factors: psychological, social or even physical factors. Psychological factors are related to permanent stress and the inability to successfully cope with di fficult situations. Social factors concern relationship struggles with family or friends and physical factors cover head injuries. Depression describes a loss of interest in every exciting and joyful aspect of everyday life. Mood disorders and mood swings are temporary mental states taking an essential part of daily events, whereas, depression is more permanent and can lead to suicide at its extreme severity levels.
Detecting emotional arousal from the sound of someone's voice is one thing -- startups like Beyond Verbal, Affectiva, and MIT spinout Cogito are leveraging natural language processing to accomplish just that. But there's an argument to be made that speech alone isn't enough to diagnose someone with depression, let alone judge its severity. Enter new research from scientists at the Indian Institute of Technology Patna and the University of Caen Normandy ("The Verbal and Non Verbal Signals of Depression -- Combining Acoustics, Text and Visuals for Estimating Depression Level"), which examines how nonverbal signs and visuals can drastically improve estimations of depression level. "The steadily increasing global burden of depression and mental illness acts as an impetus for the development of more advanced, personalized and automatic technologies that aid in its detection," the paper's authors wrote. "Depression detection is a challenging problem as many of its symptoms are covert."
Miniature Facebook banners are seen on snacks prepared for the visit by Facebook's Chief Operating Officer in Paris, France, January 17, 2017. The world's largest social media network said it plans to integrate its existing suicide prevention tools for Facebook posts into its live-streaming feature, Facebook Live, and its Messenger service. Artificial intelligence will be used to help spot users with suicidal tendencies, the company said in a blogpost on Wednesday. Facebook is already using artificial intelligence to monitor offensive material in live video streams. The company said on Wednesday that the updated tools would give an option to users watching a live video to reach out to the person directly and report the video to Facebook.