455,014 research outputs found
Voice stress analysis
In a study of the validity of eight candidate voice measures (fundamental frequency, amplitude, speech rate, frequency jitter, amplitude shimmer, Psychological Stress Evaluator scores, energy distribution, and the derived measure of the above measures) for determining psychological stress, 17 males age 21 to 35 were subjected to a tracking task on a microcomputer CRT while parameters of vocal production as well as heart rate were measured. Findings confirm those of earlier studies that increases in fundamental frequency, amplitude, and speech rate are found in speakers involved in extreme levels of stress. In addition, it was found that the same changes appear to occur in a regular fashion within a more subtle level of stress that may be characteristic, for example, of routine flying situations. None of the individual speech measures performed as robustly as did heart rate
Psychological stress measurement through voice output analysis
Audio tape recordings of selected Skylab communications were processed by a psychological stress evaluator. Strip chart tracings were read blind and scores were assigned based on characteristics reported by the manufacturer to indicate psychological stress. These scores were analyzed for their empirical relationships with operational variables in Skylab judged to represent varying degrees of situational stress. Although some statistically significant relationships were found, the technique was not judged to be sufficiently predictive to warrant its use in assessing the degree of psychological stress of crew members in future space missions
Imagery rescripting for the treatment of trauma in voice hearers: a case series
Background: High rates of trauma and post-traumatic stress disorder (PTSD) are reported in people who hear voices (auditory hallucinations). A recent metanalysis of trauma interventions in psychosis showed only small improvements in PSTD symptoms and voices. Imagery Rescripting (ImRs) may be a therapy that is more effective in this population because it generalizes over memories, which is ideal in this population with typically repeated traumas. The primary aims of this study were to investigate whether ImR reduces (1) PTSD symptoms and (2) voice frequency and distress in voice hearers. Methods: A single arm open trial study, case-series design. Twelve voice hearers with previous traumas that were thematically related to their voices participated. Brief weekly assessments (administered sessions 1-8, post-intervention, and 3-month follow-up) and longer measures (administered pre-, mid-, and post-intervention) were administered. Mixed regression analysis was used to analyze the results. Results: There was one treatment dropout. Results of the weekly measure showed significant linear reductions over time in all three primary variables - Voice Distress, Voice Frequency, and Trauma Intrusions - all with large effect sizes. These effects were maintained (and continued to improve for Trauma Intrusions) at 3-month follow-up. On the full assessment tools, all measures showed improvement over time, with five outcomes showing significant time effects: trauma, voice frequency, voice distress, voice malevolence and stress. Conclusion: The findings of the current study suggest that ImRs for PTSD symptoms is generally well tolerated and can be therapeutically beneficial among individuals who hear voices
Speaker Embeddings as Individuality Proxy for Voice Stress Detection
Since the mental states of the speaker modulate speech, stress introduced by
cognitive or physical loads could be detected in the voice. The existing voice
stress detection benchmark has shown that the audio embeddings extracted from
the Hybrid BYOL-S self-supervised model perform well. However, the benchmark
only evaluates performance separately on each dataset, but does not evaluate
performance across the different types of stress and different languages.
Moreover, previous studies found strong individual differences in stress
susceptibility. This paper presents the design and development of voice stress
detection, trained on more than 100 speakers from 9 language groups and five
different types of stress. We address individual variabilities in voice stress
analysis by adding speaker embeddings to the hybrid BYOL-S features. The
proposed method significantly improves voice stress detection performance with
an input audio length of only 3-5 seconds.Comment: 5 pages, 2 figures. Accepted at Interspeech 202
Approved: CERTIFICATION OF APPROVAL LIE DETECTION USING VOICE STRESS ANALYSIS
In the existingtechnology, there are severalways for lie detection. Voice Stress Analysis
is one of the methods for lie detection. It measures the amount of stress in the subject's
voice to detect whether the subject is being truthful or not. The VSA is reported to be
cheaper, easier to use, less invasive, less constrained in their operation and more accurate
than polygraph with 90 to 95% accuracy. In this project, the theory of Voice Stress
Analysis is used to detect deception. Developing the software of lie detection using
MATLAB will help in the investigations of law enforcers to detect deception. There are
two approaches that are used in this project which are frequency based system and energy
based system. The frequency based system detects lie using the variation in the output
graph. High variation indicates low stress or 'truthful' voice and low variation indicates
high stress or 'lying' voice. The energy based system however, detects deception in the
energy spectrum of the voice at 20 to 40 Hz frequency. A flat waveform indicates hard
stress while a sharp waveform indicates low stress. Using MATLAB, the voice is
processed and both methods are applied. The results for both methods are then compared
with the result of a polygraph test. It is found that both methods have different results on
certain voice samples and also similar results on certain voice samples. However, the
energy based system has more similar results than the frequency based system. Thus, it is
more accurate
Effects of own voice vs. another\u27s voice during progressive relaxation
The purpose of this study was to assess the difference in stress reduction between listening to one\u27s own voice and listening to another\u27s voice during taped relaxation procedures. Eighteen male undergraduates listened to relaxation tapes of their own voice, that of another person, and a control tape. Stress was measured via skin temperature. It was hypothesized that there would be a significant stress reduction (indicated by increased skin temperature) for both experimental groups and greater reduction in stress when listening to one\u27s own voice than when listening to another voice. A Latin-Square Repeated analysis revealed only a significant order effect and the results did not support the hypotheses. Methodological problems which may have led to these results are examined and clinical benefits of the hypothesized results are briefly discussed
Empathic Agent Technology (EAT)
A new view on empathic agents is introduced, named: Empathic Agent Technology (EAT). It incorporates a speech analysis, which provides an indication for the amount of tension present in people. It is founded on an indirect physiological measure for the amount of experienced stress, defined as the variability of the fundamental frequency of the human voice. A thorough review of literature is provided on which the EAT is founded. In addition, the complete processing line of this measure is introduced. Hence, the first generally applicable, completely automated technique is introduced that enables the development of truly empathic agents
Longer Than a Telephone Wire - Voice Firewalls to Counter Ubiquitous Lie Detection
Mobile computing and communication devices are open to surreptitious privacy attacks using emotion detection techniques; largely utilising work carried out in the area of voice stress analysis (VSA). This paper extends some work in the area of removing emotion cues in the voice, specifically focusing on lie detection and presents the results of a pilot study indicating that the use of mobile phones in situations of stress is common and that awareness of VSA is low. Existing strategies for the removal or modification of emotion cues, based on models of synthesis are considered and weaknesses are identified
Voice Analysis for Stress Detection and Application in Virtual Reality to Improve Public Speaking in Real-time: A Review
Stress during public speaking is common and adversely affects performance and
self-confidence. Extensive research has been carried out to develop various
models to recognize emotional states. However, minimal research has been
conducted to detect stress during public speaking in real time using voice
analysis. In this context, the current review showed that the application of
algorithms was not properly explored and helped identify the main obstacles in
creating a suitable testing environment while accounting for current
complexities and limitations. In this paper, we present our main idea and
propose a stress detection computational algorithmic model that could be
integrated into a Virtual Reality (VR) application to create an intelligent
virtual audience for improving public speaking skills. The developed model,
when integrated with VR, will be able to detect excessive stress in real time
by analysing voice features correlated to physiological parameters indicative
of stress and help users gradually control excessive stress and improve public
speaking performanceComment: 41 pages, 7 figures, 4 table
Polite mode for a virtual assistant
The use of imperative statements and commands for a voice-activated virtual assistant can set an inappropriate example to children and can potentially lead them to imitate such language in normal conversation. However, current voice-activated virtual assistants are not configured to recognize polite language which can lead to unintended responses. This disclosure describes a virtual assistant that can be configured in a polite mode. The disclosed techniques may be utilized to configure a virtual assistant such that it is responsive only to polite queries. Polite mode is enabled by use of voice stress analysis, sentiment analysis module and natural language understanding (NLU) techniques that are utilized to annotate words/phrases from a query and to determine whether the query is polite
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