Vocal and Facial Biomarkers of Depression based on Motor Incoordination and Timing

Williamson, James R.; Quatieri, Thomas F.; Helfer, Brian S.; Ciccarelli, Gregory; Mehta, Daryush D.

doi:10.1145/2661806.2661809

Cited by 160 publications

(117 citation statements)

References 36 publications

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“…This is done by using the correlation structure of the feature stream [17]. A fixed time scale of 2 frames was used to create a concatenated feature vector of the current feature vector at time j and 13 successive time delays.…”

Section: Temporal Correlation Structurementioning

confidence: 99%

“…The vocal tract can be modeled using an auto-regressive moving average model corresponding to the formants and anti-formants. These were extracted using extended Kalman smoothing [16] and used as vocal tract features for analyzing depression [17].…”

Section: Introductionmentioning

confidence: 99%

“…The two feature sets were combined using the temporal cross-correlation structure described in [17], which has been proven to be effective for detecting emotions and depression in speech and other signals.…”

Section: Introductionmentioning

confidence: 99%

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Monitoring Cognitive Workload Using Vocal Tract and Voice Source Features

Magnúsdóttir

Borský

Meier

et al. 2017

Period. Polytech. Elec. Eng. Comp. Sci.

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IntroductionMonitoring cognitive workload in individuals that are performing safety-critical jobs has huge potential, for example in aviation [1]. The relationship between cognitive workload and performance has been well studied [2] and the connection between cognitive workload and physical health has also been highlighted [3]. Clearly, it is crucial to manage cognitive workload in the modern day work environment, both in terms of performance and health. Speech processing offers the ability to monitor cognitive workload in a non-intrusive way, compared to measures such as blood pressure, heart rate, electroencephalogram or electrocardiogram. Measuring voice is easy and recent work has shown very promising results in classifying cognitive workload levels using the speech signal [4][5][6]. Successful design and implementation of such a method would provide a powerful tool to developers of cognitive infocommunications systems [7].The main objective of this work is to provide an independent verification of whether there is a link between increased cognitive workload and changes in the speech signal and, if this link exists, to characterize what part of voice is mostly affected. To do this we conducted a cognitive workload experiment where the set tasks were primarily solved using speech from 98 participants. Each participant read a standard passage of text on a computer screen and solved Stroop tasks with three difficulty levels [8]. A separate classifier of the three difficulty levels was trained for each speaker based on two sets of voice parameters comprising of the vocal tract (VT) and voice source (VS) features respectively. The conclusion is that task load does affect the speech signal and that vocal tract parameters are a better indicator of task level than voice source parameters. The context of the work is presented in Section 2 and the speech processing approach is described in Section 3. The experimental methodology and detailed results are given in Section 4 and 5. The results are summarized in Section 6 and the discussion is continued in Section 7.

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Section: Temporal Correlation Structurementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Monitoring Cognitive Workload Using Vocal Tract and Voice Source Features

Magnúsdóttir

Borský

Meier

et al. 2017

Period. Polytech. Elec. Eng. Comp. Sci.

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“…Instead of relying solely on acoustic properties or more interpretable phonological characterizations of speech such as jitter, shimmer, pitch, and formants [1,2,3], we present a neurocomputational framework that aims to model the speech production process of an individual with a particular disorder. Within a computational biophysical framework depicted in Figure 1, features may reflect internal or latent model parameters.…”

Section: Introductionmentioning

confidence: 99%

Neurophysiological Vocal Source Modeling for Biomarkers of Disease

Ciccarelli¹,

Quatieri²,

Ghosh³

2016

Interspeech 2016

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Speech is potentially a rich source of biomarkers for detecting and monitoring neuropsychological disorders. Current biomarkers typically comprise acoustic descriptors extracted from behavioral measures of source, filter, prosodic and linguistic cues. In contrast, in this paper, we extract vocal features based on a neurocomputational model of speech production, reflecting latent or internal motor control parameters that may be more sensitive to individual variation under neuropsychological disease. These features, which are constrained by neurophysiology, may be resilient to artifacts and provide an articulatory complement to acoustic features. Our features represent a mapping from a low-dimensional acoustics-based feature space to a high-dimensional space that captures the underlying neural process including articulatory commands and auditory and somatosensory feedback errors. In particular, we demonstrate a neurophysiological vocal source model that generates biomarkers of disease by modeling vocal source control. By using the fundamental frequency contour and a biophysical representation of the vocal source, we infer two neuromuscular time series whose coordination provides vocal features that are applied to depression and Parkinson's disease as examples. These vocal source coordination features alone, on a single held vowel, outperform or are comparable to other features sets and reflect a significant compression of the feature space.

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“…The AVEC 2014 data provides an opportunity for researchers to compare and test their data on a publicly available depression data. Williamson et al [32] proposed a Fig. 1.…”

Section: Introductionmentioning

confidence: 99%

A temporally piece-wise fisher vector approach for depression analysis

Dhall

Goecke

2015

2015 International Conference on Affective Computing and Intelligent Interaction (ACII)

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Abstract-Depression and other mood disorders are common, disabling disorders with a profound impact on individuals and families. Inspite of its high prevalence, it is easily missed during the early stages. Automatic depression analysis has become a very active field of research in the affective computing community in the past few years. This paper presents a framework for depression analysis based on unimodal visual cues. Temporally piece-wise Fisher Vectors (FV) are computed on temporal segments. As a low-level feature, block-wise Local Binary PatternThree Orthogonal Planes descriptors are computed. Statistical aggregation techniques are analysed and compared for creating a discriminative representative for a video sample. The paper explores the strength of FV in representing temporal segments in a spontaneous clinical data. This creates a meaningful representation of the facial dynamics in a temporal segment. The experiments are conducted on the Audio Video Emotion Challenge (AVEC) 2014 German speaking depression database. The superior results of the proposed framework show the effectiveness of the technique as compared to the current state-of-art.

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Vocal and Facial Biomarkers of Depression based on Motor Incoordination and Timing

Cited by 160 publications

References 36 publications

Monitoring Cognitive Workload Using Vocal Tract and Voice Source Features

Monitoring Cognitive Workload Using Vocal Tract and Voice Source Features

Neurophysiological Vocal Source Modeling for Biomarkers of Disease

A temporally piece-wise fisher vector approach for depression analysis

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