103,111 research outputs found
A hierarchical attention network-based approach for depression detection from transcribed clinical interviews
The high prevalence of depression in society has given rise to a need for new digital tools that can aid its early detection. Among other effects, depression impacts the use of language. Seeking to exploit this, this work focuses on the detection of depressed and non-depressed individuals through the analysis of linguistic information extracted from transcripts of clinical interviews with a virtual agent. Specifically, we investigated the advantages of employing hierarchical attention-based networks for this task. Using Global Vectors (GloVe) pretrained word embedding models to extract low-level representations of the words, we compared hierarchical local-global attention networks and hierarchical contextual attention networks. We performed our experiments on the Distress Analysis Interview Corpus - Wizard of Oz (DAIC-WoZ) dataset, which contains audio, visual, and linguistic information acquired from participants during a clinical session. Our results using the DAIC-WoZ test set indicate that hierarchical contextual attention networks are the most suitable configuration to detect depression from transcripts. The configuration achieves an Unweighted Average Recall (UAR) of .66 using the test set, surpassing our baseline, a Recurrent Neural Network that does not use attention.Funding by EU- sustAGE (826506), EU-RADAR-CNS (115902), Key Program of the Natural Science Foundation of Tianjin, CHINA (18JCZDJC36300) and BMW Group Research
Pages 221-225
https://www.isca-speech.org/archive/Interspeech_2019/index.htm
Classifying Dementia in the Presence of Depression: A Cross-Corpus Study
Automated dementia screening enables early detection and intervention,
reducing costs to healthcare systems and increasing quality of life for those
affected. Depression has shared symptoms with dementia, adding complexity to
diagnoses. The research focus so far has been on binary classification of
dementia (DEM) and healthy controls (HC) using speech from picture description
tests from a single dataset. In this work, we apply established baseline
systems to discriminate cognitive impairment in speech from the semantic Verbal
Fluency Test and the Boston Naming Test using text, audio and emotion
embeddings in a 3-class classification problem (HC vs. MCI vs. DEM). We perform
cross-corpus and mixed-corpus experiments on two independently recorded German
datasets to investigate generalization to larger populations and different
recording conditions. In a detailed error analysis, we look at depression as a
secondary diagnosis to understand what our classifiers actually learn.Comment: Accepted at INTERSPEECH 202
The Verbal and Non Verbal Signals of Depression -- Combining Acoustics, Text and Visuals for Estimating Depression Level
Depression is a serious medical condition that is suffered by a large number
of people around the world. It significantly affects the way one feels, causing
a persistent lowering of mood. In this paper, we propose a novel
attention-based deep neural network which facilitates the fusion of various
modalities. We use this network to regress the depression level. Acoustic, text
and visual modalities have been used to train our proposed network. Various
experiments have been carried out on the benchmark dataset, namely, Distress
Analysis Interview Corpus - a Wizard of Oz (DAIC-WOZ). From the results, we
empirically justify that the fusion of all three modalities helps in giving the
most accurate estimation of depression level. Our proposed approach outperforms
the state-of-the-art by 7.17% on root mean squared error (RMSE) and 8.08% on
mean absolute error (MAE).Comment: 10 pages including references, 2 figure
A habituation account of change detection in same/different judgments
We investigated the basis of change detection in a short-term priming task. In two experiments, participants were asked to indicate whether or not a target word was the same as a previously presented cue. Data from an experiment measuring magnetoencephalography failed to find different patterns for “same” and “different” responses, consistent with the claim that both arise from a common neural source, with response magnitude defining the difference between immediate novelty versus familiarity. In a behavioral experiment, we tested and confirmed the predictions of a habituation account of these judgments by comparing conditions in which the target, the cue, or neither was primed by its presentation in the previous trial. As predicted, cue-primed trials had faster response times, and target-primed trials had slower response times relative to the neither-primed baseline. These results were obtained irrespective of response repetition and stimulus–response contingencies. The behavioral and brain activity data support the view that detection of change drives performance in these tasks and that the underlying mechanism is neuronal habituation
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