Verbal and Nonverbal Correlates for Depression: A Review

Balsters, Martijn; Krahmer, Emiel; Swerts, Marc; Vingerhoets, A.J.J.M.

doi:10.2174/157340012800792966

Cited by 33 publications

(20 citation statements)

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“…Patients, therefore, live the therapeutic relationship and the alliance construction by manifesting different experiential and behavioral modalities through verbal and non-verbal communication (Tomicic et al, 2009;Valdés and Krause, 2015), as an expression of their psychological processes and symptoms (Valdés, 2014;Elvevåg et al, 2016). Depressed patients, in particular, show difficulties in developing and maintaining the TA because of the specific verbal and non-verbal correlates that define their communicative behaviors (Balsters et al, 2012;Smirnova et al, 2018). According to the psychodynamic approach, these behaviors reflect the broad range of depressed patients' defensive, adaptation, and cognition styles deriving from the early cognitive-affective representations where anger and aggression are predominant (Levy and Wasserman, 2009).…”

Section: Introductionmentioning

confidence: 99%

The Action of Verbal and Non-verbal Communication in the Therapeutic Alliance Construction: A Mixed Methods Approach to Assess the Initial Interactions With Depressed Patients

2020

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In psychodynamic psychotherapy, verbal (structures and intents) and non-verbal (voice and interruptions) dimensions of communication intertwine conveying information and determining the mutual regulation between therapist and patient through conversational sequences. The communication components interplay is the foundation for building the therapeutic alliance, a relational dimension that predicts a psychotherapy outcome and change, influenced by patient-therapist exchanges from the initial stages of their encounter. Depressed patients present specific verbal and non-verbal communication and show difficulties in developing and maintaining the therapeutic alliance. Based on the reviewed literature, the main aim of this study was to analyze how the action of specific communicative modes, implemented by the therapist and depressed patients, affect the reciprocal construction of the early therapeutic alliance by each participant during the mutual regulation processes. We employed a mixed methods approach based on a systematic observation of communication and alliance ruptures and repairs within the audio recordings and verbatim transcripts of 20 psychotherapy sessions (6,232 speaking turns) with seven depressed patients. The observational design was nomothetic, follow-up, and multidimensional. The choice of methodology is justified because we developed a comprehensive procedure that integrates an ad hoc indirect observation system (the Communicative Modes Analysis System in Psychotherapy), analyzing verbal and non-verbal communication, and an observational tool with deductive categories (the Collaborative Interactions Scale-Revised), assessing the therapeutic alliance construction. Once we confirmed the intra-and inter-observer reliability for the ad hoc system and the inter-rater reliability for the tool with deductive (or theoretical) categories, we performed descriptive statistics (to describe quantitatively communicative modes and alliance Del Giacco et al. Communication Acts on Therapeutic Alliance ruptures and repairs), lag sequential analysis (to detect stable patterns in communication-alliance interactions), and polar coordinate analysis (to identify significant relationships between communicative modes and alliance ruptures and repairs). Results confirm that the therapist's verbal (asking and exploring) and non-verbal (elaborating and cooperatively interrupting) modes and the depressed patients' verbal (asserting and exploring) and non-verbal (expressing emotions and cooperatively interrupting) modes determine stable patterns and significant associations with collaborative behaviors connected to the reciprocal construction of alliance by each participant. All this may provide professionals with useful information to increase the psychotherapy effectiveness with depressed patients.

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Section: Introductionmentioning

confidence: 99%

The Action of Verbal and Non-verbal Communication in the Therapeutic Alliance Construction: A Mixed Methods Approach to Assess the Initial Interactions With Depressed Patients

2020

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“…Speech is considered a key component in any behavioural based diagnostic tool (Cummins et al, 2015). Speech affected by depression is often characterised by decreases in verbal activity; decreases in utterance length, and a reduction in speech rate and long silent pauses (Balsters et al, 2012;Hall et al, 1995;Sobin and Sackeim, 1997).…”

Section: Biomarkers and Behavioural Signals For Depressionmentioning

confidence: 99%

Analysis of acoustic space variability in speech affected by depression

Cummins

Sethu

Epps

et al. 2015

Speech Communication

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The spectral and energy properties of speech have consistently been observed to change with a speaker's level of clinical depression. This has resulted in spectral and energy based features being a key component in many speech-based classification and prediction systems. However there has been no in-depth investigation into understanding how acoustic models of spectral features are affected by depression. This paper investigates the hypothesis that the effects of depression in speech manifest as a reduction in the spread of phonetic events in acoustic space as modelled by Gaussian Mixture Models (GMM) in combination with Mel Frequency Cepstral Coefficients (MFCC).Our investigation uses three measures of acoustic variability: Average Weighted Variance (AWV), Acoustic Movement (AM) and Acoustic Volume, which attempt to model depression specific acoustic variations (AWV and Acoustic Volume), or the trajectory of a speech in the acoustic space (AM).Within our analysis we present the Probabilistic Acoustic Volume (PAV) a novel method for robustly estimating Acoustic Volume using a Monte Carlo sampling of the feature distribution being * Corresponding Author modelled. We show that using an array of PAV points we gain insights into how the concentration of the feature vectors in the feature space changes with depression. Key results -found on two commonly used depression corpora -consistently indicate that as a speaker's level of depression increases there are statistically significantly reductions in both AWV (-0.44 ≤ r s ≤ -0.18 with p < .05) and AM (-0.26 ≤ r s ≤ -0.19 with p < .05) values, indicating a decrease in localised acoustic variance and smoothing in acoustic trajectory respectively. Further there are also statistically significant reductions (-0.32 ≤ r s ≤ -0.20 with p < .05) in Acoustic Volume measures and strong statistical evidence (-0.48 ≤ r s ≤ -0.23 with p < .05) that the MFCC feature space becomes more concentrated.Quantifying these effects is expected to be a key step towards building an objective classification or prediction system which is robust to many of the unwanted -in terms of depression analysissources of variability modulated into a speech signal.

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“…Although over 60% of human communication is believed to be non-verbal, large parts of this channel of communication are beyond conscious control, and current diagnostic methods for depression do not utilize this extra information [14].…”

Section: Behavioural Markers Associated With Derpessionmentioning

confidence: 99%

“…Speech in patients with depression is often described having diminished prosody, forcing it to sound dull, monotonous and "lifeless" [15]. This leads to depressed speech having longer pauses, decreased utterance length and a reduced speech rate [14], [15]. It has been hypothesized that it takes more articulatory effort for a depressed individual to produce and sustain speech; this is evident in decreased formant frequencies often reported with increasing levels of depression [4], [16].…”

Section: Non Linguistic Audio Cuesmentioning

confidence: 99%

Diagnosis of depression by behavioural signals

Cummins

Joshi

Dhall

et al. 2013

Proceedings of the 3rd ACM International Workshop on Audio/Visual Emotion Challenge

104

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Quantifying behavioural changes in depression using affective computing techniques is the first step in developing an objective diagnostic aid, with clinical utility, for clinical depression. As part of the AVEC 2013 Challenge, we present a multimodal approach for the Depression Sub-Challenge using a GMM-UBM system with three different kernels for the audio subsystem and Space Time Interest Points in a Bag-of-Words approach for the vision subsystem. These are then fused at the feature level to form the combined AV system. Key results include the strong performance of acoustic audio features and the bag-of-words visual features in predicting an individual's level of depression using regression. Interestingly, in the context of the small amount of literature on the subject, is that our feature level multimodal fusion technique is able to outperform both the audio and visual challenge baselines.

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Verbal and Nonverbal Correlates for Depression: A Review

Cited by 33 publications

References 0 publications

The Action of Verbal and Non-verbal Communication in the Therapeutic Alliance Construction: A Mixed Methods Approach to Assess the Initial Interactions With Depressed Patients

The Action of Verbal and Non-verbal Communication in the Therapeutic Alliance Construction: A Mixed Methods Approach to Assess the Initial Interactions With Depressed Patients

Analysis of acoustic space variability in speech affected by depression

Diagnosis of depression by behavioural signals

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