Psychomotor cues for depression screening

Shah, Zafi Sherhan; Sidorov, Kirill; Marshall, David

doi:10.1109/icdsp.2017.8096052

Cited by 4 publications

(4 citation statements)

References 30 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Feature aggregation is an approach through which LLDs are summarised to create features which provide global information about the speech recordings. While several feature aggregation methods exist, such as functionals [8], GMM supervectors [9], Vectors of Locally Aggregated Descriptors (VLADs) [10], i-vectors [11] etc., we opt to use Fisher Vector encoding for aggregating spectral LLDs based on our previous experience: we found them effective for classifying between individuals with and without depression [12], as well as prediction of their depression severity [13].…”

Section: Spectral Modelling With Fisher Vectorsmentioning

confidence: 99%

“…While FV encoding was originally proposed by [14] for building visual vocabularies, it has become popular for a variety of applications in the field of social signal processing, such as depression recognition [15,16,12,13], emotion recognition [17] as well as recent Interspeech Computational Paralinguistics (Com-ParE) challenges [18,19,20].…”

Section: Spectral Modelling With Fisher Vectorsmentioning

confidence: 99%

“…L ∈ {2, 5, 10, 50}. It is also worth mentioning that both us [12] and Kaya et al [20] concurrently proposed, albeit at different conferences, the use of weighted extreme learning machines for tasks pertaining to social signal processing.…”

Section: Ensembles Of Weighted Extreme Learning Machinesmentioning

confidence: 99%

See 2 more Smart Citations

Computational Paralinguistics: Automatic Assessment of Emotions, Mood and Behavioural State from Acoustics of Speech

et al. 2018

Self Cite

View full text Add to dashboard Cite

Paralinguistic analysis of speech remains a challenging task due to the many confounding factors which affect speech production. In this paper, we address the Interspeech 2018 Computational Paralinguistics Challenge (ComParE) which aims to push the boundaries of sensitivity to non-textual information that is conveyed in the acoustics of speech. We attack the problem on several fronts. We posit that a substantial amount of paralinguistic information is contained in spectral features alone. To this end, we use a large ensemble of Extreme Learning Machines for classification of spectral features. We further investigate the applicability of (an ensemble of) CNN-GRUs networks to model temporal variations therein. We report on the details of the experiments and the results for three ComParE sub-challenges: Atypical Affect, Self-Assessed Affect, and Crying. Our results compare favourably and in some cases exceed the published state-of-the-art performance.

show abstract

Section: Spectral Modelling With Fisher Vectorsmentioning

confidence: 99%

Section: Spectral Modelling With Fisher Vectorsmentioning

confidence: 99%

See 1 more Smart Citation

Computational Paralinguistics: Automatic Assessment of Emotions, Mood and Behavioural State from Acoustics of Speech

et al. 2018

Self Cite

View full text Add to dashboard Cite

show abstract

“…Digital biomarkers of movement in PTSD revealed an association with suppressed motor activity to neutral stimuli (Litz, Orsillo, Kaloupek, & Weathers, 2000) and heightened arousal (Blechert et al, 2013) and increased eye blink (McTeague et al, 2010) and increased fixation on trauma-related stimuli (Felmingham, Rennie, Manor, & Bryant, 2011). Digital biomarkers of movement in MDD have also been examined (Anis, Zakia, Mohamed, & Jeffrey, 2018;Bhatia, Goecke, Hammal, & Cohn, 2019;Dibeklioğlu, Hammal, & Cohn, 2017;Shah, Sidorov, & Marshall, 2017), such as psychomotor retardation (Syed, Sidorov, & Marshall, 2017).…”

Section: Introductionmentioning

confidence: 99%

Deep learning-based classification of posttraumatic stress disorder and depression following trauma utilizing visual and auditory markers of arousal and mood

et al. 2020

View full text Add to dashboard Cite

Background Visual and auditory signs of patient functioning have long been used for clinical diagnosis, treatment selection, and prognosis. Direct measurement and quantification of these signals can aim to improve the consistency, sensitivity, and scalability of clinical assessment. Currently, we investigate if machine learning-based computer vision (CV), semantic, and acoustic analysis can capture clinical features from free speech responses to a brief interview 1 month post-trauma that accurately classify major depressive disorder (MDD) and posttraumatic stress disorder (PTSD). Methods N = 81 patients admitted to an emergency department (ED) of a Level-1 Trauma Unit following a life-threatening traumatic event participated in an open-ended qualitative interview with a para-professional about their experience 1 month following admission. A deep neural network was utilized to extract facial features of emotion and their intensity, movement parameters, speech prosody, and natural language content. These features were utilized as inputs to classify PTSD and MDD cross-sectionally. Results Both video- and audio-based markers contributed to good discriminatory classification accuracy. The algorithm discriminates PTSD status at 1 month after ED admission with an AUC of 0.90 (weighted average precision = 0.83, recall = 0.84, and f1-score = 0.83) as well as depression status at 1 month after ED admission with an AUC of 0.86 (weighted average precision = 0.83, recall = 0.82, and f1-score = 0.82). Conclusions Direct clinical observation during post-trauma free speech using deep learning identifies digital markers that can be utilized to classify MDD and PTSD status.

show abstract