Voice Stress Analysis

Rothkrantz, Léon J. M.; Wiggers, Pascal; Wees, Jan-Willem A. van; Vark, Robert J. van

doi:10.1007/978-3-540-30120-2_57

Cited by 51 publications

(35 citation statements)

References 11 publications

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“…Using the clean signal, the following features were extracted and compared to the ground truth: pitch (F0), energy (E) (Cowie et al, 2001;Scherer, 2003;Ververidis and Kotropoulos, 2006), high-frequency energy (HFE) (Cowie et al, 2001;Rothkrantz et al, 2004), and zero-crossings rate (ZC) (Kedem, 1986;Rothkrantz et al, 2004). Although there is no general consensus regarding the best speech parameters for stress detection, there is a fair amount of evidence for the affective information in these features.…”

Section: Methodsmentioning

confidence: 99%

Towards Semi-Automated Assistance for the Treatment of Stress Disorders

Sluis

Broek

Dijkstra

2010

Proceedings of the Third International Conference on Health Informatics

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Abstract:People who suffer from a stress disorder have a severe handicap in daily life. In addition, stress disorders are complex and consequently, hard to define and hard to treat. Semi-automatic assistance was envisioned that helps in the treatment of a stress disorder. Speech was considered to provide an excellent tool for providing an objective, unobtrusive emotion measure. Speech from 25 patients suffering from a stress disorder was recorded while they participated in two storytelling sessions. As a subjective measure, the Subjective Unit of Distress (SUD) was determined, which enabled the validation of derived speech features. A regression model with four speech parameters (i.e., signal, power, zero crossing ratio, and pitch), was able to explain 70% of the variance in the SUD measure. As such it lays the foundation for semi-automated assistance for the treatment of patients with stress disorders.

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Section: Methodsmentioning

confidence: 99%

Towards Semi-Automated Assistance for the Treatment of Stress Disorders

Sluis

Broek

Dijkstra

2010

Proceedings of the Third International Conference on Health Informatics

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“…In our own research, we measured five important characteristics of speech: (i) the power (or intensity or energy) of the speech signal [13,34]; (ii) its fundamental frequency (F 0 ) or pitch [13,29,34,45,55]; (iii) the zero-crossings rate [24,41]; (iv) its wave amplitude [34,45]; and (v) the high-frequency power [4,13,34,41].…”

Section: Speech Signal Featuresmentioning

confidence: 99%

Cross-validation of bimodal health-related stress assessment

Broek

Sluis

Dijkstra

2011

Pers Ubiquit Comput

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This study explores the feasibility of objective and ubiquitous stress assessment. 25 post-traumatic stress disorder patients participated in a controlled storytelling (ST) study and an ecologically valid reliving (RL) study. The two studies were meant to represent an early and a late therapy session, and each consisted of a ''happy'' and a ''stress triggering'' part. Two instruments were chosen to assess the stress level of the patients at various point in time during therapy: (i) speech, used as an objective and ubiquitous stress indicator and (ii) the subjective unit of distress (SUD), a clinically validated Likert scale. In total, 13 statistical parameters were derived from each of five speech features: amplitude, zero-crossings, power, highfrequency power, and pitch. To model the emotional state of the patients, 28 parameters were selected from this set by means of a linear regression model and, subsequently, compressed into 11 principal components. The SUD and speech model were cross-validated, using 3 machine learning algorithms. Between 90% (2 SUD levels) and 39% (10 SUD levels) correct classification was achieved. The two sessions could be discriminated in 89% (for ST) and 77% (for RL) of the cases. This report fills a gap between laboratory and clinical studies, and its results emphasize the usefulness of Computer Aided Diagnostics (CAD) for mental health care.

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“…SVM and HMM based classifiers produce significantly better results on SUSAS database from the previous approaches. A recent research of Rothkrantz et al [7] focuses on studying the effect of the workload of speech production by making use of psychological experimental setup.…”

Section: Related Workmentioning

confidence: 99%

A New Fangled Insinuation for Stress Affect Speech Classification

Nachamai¹,

Santhanam²,

Sumathi³

2010

IJCA

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Demarcation in human interaction is through two channels: one transmits explicit messages; the other transmits implicit messages about the speakers themselves knowingly or unknowingly. Both linguistics and technology have invested enormous effort in trying to understand the first (explicit) channel, but the second (implicit) is not as well understood. First, building an emotion detection system makes it possible to assess the extent to which theoretical proposals explain people's everyday competence in understanding emotion. Second, model building enforces coherence. It is true that emotions play an important role in the making of speech. The deduction of emotions from speech is of recent origin and it is the primary focus of this research paper.

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Voice Stress Analysis

Cited by 51 publications

References 11 publications

Towards Semi-Automated Assistance for the Treatment of Stress Disorders

Towards Semi-Automated Assistance for the Treatment of Stress Disorders

Cross-validation of bimodal health-related stress assessment

A New Fangled Insinuation for Stress Affect Speech Classification

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