Vocal Indicators of Affective Disorders

Scherer, Klaus R.; Zei, B.

doi:10.1159/000288082

Cited by 54 publications

(50 citation statements)

References 6 publications

(8 reference statements)

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“…There is evidence that sadness and depression are associated with a decrease in loudness [9], resulting in lower loudness for depressed. Since the loudness is intimately related to sound intensity, both features will be investigated here.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Detecting depression: A comparison between spontaneous and read speech

Alghowinem

Goecke

Wagner

et al. 2013

2013 IEEE International Conference on Acoustics, Speech and Signal Processing

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Major depressive disorders are mental disorders of high prevalence, leading to a high impact on individuals, their families, society and the economy. In order to assist clinicians to better diagnose depression, we investigate an objective diagnostic aid using affective sensing technology with a focus on acoustic features. In this paper, we hypothesise that (1) classifying the general characteristics of clinical depression using spontaneous speech will give better results than using read speech, (2) that there are some acoustic features that are robust and would give good classification results in both spontaneous and read, and (3) that a 'thin-slicing' approach using smaller parts of the speech data will perform similarly if not better than using the whole speech data. By examining and comparing recognition results for acoustic features on a real-world clinical dataset of 30 depressed and 30 control subjects using SVM for classification and a leave-one-out cross-validation scheme, we found that spontaneous speech has more variability, which increases the recognition rate of depression. We also found that jitter, shimmer, energy and loudness feature groups are robust in characterising both read and spontaneous depressive speech. Remarkably, thin-slicing the read speech, using either the beginning of each sentence or the first few sentences performs better than using all reading task data.

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Section: Related Workmentioning

confidence: 99%

“…Since the loudness is intimately related to sound intensity, both features will be investigated here. Jitter and shimmer voice features have been analysed for depression, finding higher jitter in depression caused by the irregularity of the vocal fold vibrations [9]. On the other hand, shimmer is lower for depressed [10].…”

Section: Related Workmentioning

confidence: 99%

Detecting depression: A comparison between spontaneous and read speech

Alghowinem

Goecke

Wagner

et al. 2013

2013 IEEE International Conference on Acoustics, Speech and Signal Processing

View full text Add to dashboard Cite

show abstract

“…Previous findings such as these suggest parameters for controlling the perception of neuroticism. For example, neuroticism findings suggest a content polarity parameter for selecting negative content, as well as a repetitions parameter [33,40]. Neurotics are also more likely to engage in self touch [43] and less likely to gesture towards others [3].…”

Section: Introductionmentioning

confidence: 99%

Don’t Scratch! Self-adaptors Reflect Emotional Stability

Neff

Toothman

Bowmani

et al. 2011

Lecture Notes in Computer Science

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Abstract. A key goal in agent research is to be able to generate multimodal characters that can reflect a particular personality. The Big Five model of personality provides a framework for codifying personality variation. This paper reviews findings in the psychology literature to understand how the Big Five trait of emotional stability correlates with changes in verbal and nonverbal behavior. Agent behavior was modified based on these findings and a perceptual study was completed to determine if these changes lead to the controllable perception of emotional stability in virtual agents. The results reveal how language variation and the use of self-adaptors can be used to increase or decrease the perceived emotional stability of an agent. Self-adaptors are movements that often involve self-touch, such as scratching or bending one's fingers backwards in an unnatural brace. These results provide guidance on how agent designers can create particular characters, including indicating that for particular personality types, it is important to also produce typically non-communicative gestural behavior, such as the self-adaptors studied.

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“…really) and exclamation marks (Oberlander and Gill 2004b;Mehl et al 2006). There are filled pauses and stuttering markers because introverts and neurotics produce more filled pauses and disfluencies (Scherer 1979;Weaver 1998;Scherer 1981). Neuroticism is associated with frustration and acquiescence, which are modeled with the expletives and acknowledgment parameters (Weaver 1998;Oberlander and Gill 2004b).…”

Section: Pragmatic Marker Insertionmentioning

confidence: 99%

Individual Differences

College¹,

Eysenck²

2014

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Conversation is an essential component of social behavior, one of the primary means by which humans express intentions, beliefs, emotions, attitudes and personality. Thus the development of systems to support natural conversational interaction has been a long term research goal. In natural conversation, humans adapt to one another across many levels of utterance production via processes variously described as linguistic style matching, entrainment, alignment, audience design, and accommodation. A number of recent studies strongly suggest that dialogue systems that adapted to the user in a similar way would be more effective. However, a major research challenge in this area is the ability to dynamically generate user-adaptive utterance variations. As part of a personality-based user adaptation framework, this article describes personage, a highly parameterizable generator which provides a large number of parameters to support adaptation to a user's linguistic style. We show how we can systematically apply results from psycholinguistic studies that document the linguistic reflexes of personality, in order to develop models to control personage's parameters, and produce utterances matching particular personality profiles. When we evaluate these outputs with human judges, the results indicate that humans perceive the personality of system utterances in the way that the system intended.

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Vocal Indicators of Affective Disorders

Cited by 54 publications

References 6 publications

Detecting depression: A comparison between spontaneous and read speech

Detecting depression: A comparison between spontaneous and read speech

Don’t Scratch! Self-adaptors Reflect Emotional Stability

Individual Differences

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