Stress Detection from Speech Using Spectral Slope Measurements

Simantiraki, Olympia; Giannakakis, Giorgos; Pampouchidou, Anastasia; Tsiknakis, Manolis

doi:10.1007/978-3-319-74935-8_5

Cited by 21 publications

(14 citation statements)

References 23 publications

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“…Research on spectral profile parameters suggests they are mostly associated with vocal valence, such as angry speech, as well as the control over emotions (Eyben et al, 2016;Goudbeek & Scherer, 2010). Spectral slope and pitch variation have also been associated with emotional stress (Simantiraki, Giannakakis, Pampouchidou, & Tsiknakis, 2016). Frequency related parameters have been associated with arousal, alertness and engagement in the conversation (Goudbeek & Scherer, 2010).…”

Section: Discussionmentioning

confidence: 99%

Acoustic speech markers for schizophrenia-spectrum disorders: a diagnostic and symptom-recognition tool

Boer

Voppel²,

Brederoo³

et al. 2021

Psychol. Med.

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Background Clinicians routinely use impressions of speech as an element of mental status examination. In schizophrenia-spectrum disorders, descriptions of speech are used to assess the severity of psychotic symptoms. In the current study, we assessed the diagnostic value of acoustic speech parameters in schizophrenia-spectrum disorders, as well as its value in recognizing positive and negative symptoms. Methods Speech was obtained from 142 patients with a schizophrenia-spectrum disorder and 142 matched controls during a semi-structured interview on neutral topics. Patients were categorized as having predominantly positive or negative symptoms using the Positive and Negative Syndrome Scale (PANSS). Acoustic parameters were extracted with OpenSMILE, employing the extended Geneva Acoustic Minimalistic Parameter Set, which includes standardized analyses of pitch (F0), speech quality and pauses. Speech parameters were fed into a random forest algorithm with leave-ten-out cross-validation to assess their value for a schizophrenia-spectrum diagnosis, and PANSS subtype recognition. Results The machine-learning speech classifier attained an accuracy of 86.2% in classifying patients with a schizophrenia-spectrum disorder and controls on speech parameters alone. Patients with predominantly positive v. negative symptoms could be classified with an accuracy of 74.2%. Conclusions Our results show that automatically extracted speech parameters can be used to accurately classify patients with a schizophrenia-spectrum disorder and healthy controls, as well as differentiate between patients with predominantly positive v. negatives symptoms. Thus, the field of speech technology has provided a standardized, powerful tool that has high potential for clinical applications in diagnosis and differentiation, given its ease of comparison and replication across samples.

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Section: Discussionmentioning

confidence: 99%

Acoustic speech markers for schizophrenia-spectrum disorders: a diagnostic and symptom-recognition tool

Boer

Voppel²,

Brederoo³

et al. 2021

Psychol. Med.

View full text Add to dashboard Cite

show abstract

“…With respect to the CRM stimuli used in this study, we used the average power spectral density for all CRM speech stimuli to investigate acoustic correlates of F0 and VTL. The F0 was estimated by the first peak of the spectral envelope ( Flanagan, 1965 ), and the spectral slope was estimated by the slope of the regression line fitted to the spectral envelope using the least square error method ( Simantiraki et al , 2018 ). The result showed clear F0 differences between male (F0: 100 ± 7 Hz) and female (F0: 204 ± 12 Hz) talkers but similar spectral slopes among talkers (male talkers: −14.3 ± 0.3 dB/octave; female talkers: −14.1 ± 0.1 dB/octave).…”

Section: Discussionmentioning

confidence: 99%

Interaction between voice-gender difference and spatial separation in release from masking in multi-talker listening environments

Bridges

Schoenfeld

et al. 2021

JASA Express Letters

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Voice-gender difference and spatial separation between talkers are important cues for speech segregation in multi-talker listening environments. The goal of this study was to investigate the interactions of these two cues to explore how they influence masking release in normal hearing listeners. Speech recognition thresholds in competing speech were measured, and masking release benefits by either voice-gender difference or spatial separation cues were calculated. Results revealed that the masking releases by those two cues are inversely related as a function of spatial separation, with a gender-specific difference of transition between the two types of masking release.

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“…Voice patterns can be quite different for a person under stress or not. Multiple features related to the voice patterns could be altered when stress is present such as the changes in pitch [ 63 ], tone and speaking rate, or even the words they choose in the speech.…”

Section: Assessment For Pain and Stressmentioning

confidence: 99%

Pain and Stress Detection Using Wearable Sensors and Devices—A Review

Chen

Abbod

Shieh

2021

Sensors

128

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Pain is a subjective feeling; it is a sensation that every human being must have experienced all their life. Yet, its mechanism and the way to immune to it is still a question to be answered. This review presents the mechanism and correlation of pain and stress, their assessment and detection approach with medical devices and wearable sensors. Various physiological signals (i.e., heart activity, brain activity, muscle activity, electrodermal activity, respiratory, blood volume pulse, skin temperature) and behavioral signals are organized for wearables sensors detection. By reviewing the wearable sensors used in the healthcare domain, we hope to find a way for wearable healthcare-monitoring system to be applied on pain and stress detection. Since pain leads to multiple consequences or symptoms such as muscle tension and depression that are stress related, there is a chance to find a new approach for chronic pain detection using daily life sensors or devices. Then by integrating modern computing techniques, there is a chance to handle pain and stress management issue.

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Stress Detection from Speech Using Spectral Slope Measurements

Cited by 21 publications

References 23 publications

Acoustic speech markers for schizophrenia-spectrum disorders: a diagnostic and symptom-recognition tool

Acoustic speech markers for schizophrenia-spectrum disorders: a diagnostic and symptom-recognition tool

Interaction between voice-gender difference and spatial separation in release from masking in multi-talker listening environments

Pain and Stress Detection Using Wearable Sensors and Devices—A Review

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