TOWARDS AN ARTIFICIAL THERAPY ASSISTANT

Measuring Excessive Stress from Speech

Frans van der Sluis, Egon L. van den Broek

Human-Media Interaction (HMI), University of Twente, P.O. Box 217, 7500 AE Enschede, The Netherlands

Ton Dijkstra

Donders Institute for Brain, Cognition, and Behavior, Radboud University

P.O. Box 9104, 6500 HE Nijmegen, The Netherlands

Keywords:

Stress, Psychiatry, Diagnosis, Speech, Agent, Model.

Abstract:

The measurement of (excessive) stress is still a challenging endeavor. Most tools rely on either introspection

or expert opinion and are, therefore, often less reliable or a burden on the patient. An objective method could

relieve these problems and, consequently, assist diagnostics. Speech was considered an excellent candidate

for an objective, unobtrusive measure of emotion. True stress was successfully induced, using two storytelling

sessions performed by 25 patients suffering from a stress disorder. When reading either a happy or a sad story,

different stress levels were reported using the Subjective Unit of Distress (SUD). A linear regression model

consisting of the high-frequency energy, pitch, and zero crossings of the speech signal was able to explain

70% of the variance in the subjectively reported stress. The results demonstrate the feasibility of an objective

measurement of stress in speech. As such, the foundation for an Artiﬁcial Therapeutic Agent is laid, capable

of assisting therapists through an objective measurement of experienced stress.

1 INTRODUCTION

In 1975, Malcolm Lader stated: “Psychiatric re-

search has been unsuccessful in developing scientiﬁc

methods of its own but has relied on a series of tech-

niques borrowed from other disciplines. Too often the

outside discipline has been chosen because of its rel-

evance, and the high hopes at the outset of such stud-

ies have lessened as concrete advances have failed to

materialise.” (Lader, 1975). Since these words were

published signiﬁcant progress has been made in both

science and engineering. However, psychiatry is still

struggling in some sense. On the one hand, it is now

generally accepted that mind and body go hand in

hand. Consequently, for example, psychopharmacy

have gained in popularity since Lader’s words. On the

other hand, science is not even close to truly under-

standing the relation between mind and body, which

is illustrated by the lack of computational models.

Computational models have been proposed in the

shape of decision support systems and agents. In

some branches of industry these models have been

successfully employed but not in psychiatry. Various

reasons for this lack of success can be opted; for ex-

ample, overly complex models, unjustiﬁed simpliﬁ-

cations, a lack of validation procedures, and simply a

lack of domain knowledge. To prevent from suffering

the same pitfalls, this study limits its aims. The core

concept under investigation will be: stress.

The usage of the term stress has been liberal, leav-

ing it as a poorly deﬁned term with many deﬁni-

tions. However, as a common denominator, the differ-

ent views build upon some form of a process model,

in which “environmental demands tax or exceed the

adaptive capacity of an organism, resulting in psycho-

logical and biological changes that may place per-

sons at risk for disease” (Cohen and Oviatt, 2002).

Several traditions have emphasized different aspects

of this process: environmental, psychological, and

biological aspects. These can be roughly translated

to, respectively, the stressor (Kessler, 1997), the ap-

praisal (Lazarus, 1993), and their (often physiologi-

cal) responses (Cohen and Oviatt, 2002).

Table 1 gives an overview of a few prevalent

357

van der Sluis F., L. van den Broek E. and Dijkstra T..

TOWARDS AN ARTIFICIAL THERAPY ASSISTANT - Measuring Excessive Stress from Speech.

DOI: 10.5220/0003175203570363

In Proceedings of the International Conference on Health Informatics (HEALTHINF-2011), pages 357-363

ISBN: 978-989-8425-34-8

 2011 SCITEPRESS (Science and Technology Publications, Lda.)

Table 1: Some Stress-Related Psychiatric Disorders.

Post-Traumatic Stress Disorder (PTSD) is caused by a severe trauma, originating from a range of situations;

e.g., warfare, natural disasters, inter-personal violence such as sexual, physical, and emotional abuse, intimate

partner violence, and collective violence. Key characteristics of PTSD are a persistent reexperience of the

stressor, and persistent symptoms of increased arousal (American Psychiatric Association, 2000).

Depression cannot always be related to a speciﬁc cause, though several contributing factors have been identi-

ﬁed: e.g., genetic vulnerability and unavoidability of stress. More speciﬁc, certain stressful life events (e.g.,

job loss, widowhood) can lead to a state of depression. Furthermore, chronic role-related stress is signiﬁcantly

associated with chronically depressed mood (Kessler, 1997). Important to note is that the experience of stress

is associated with the onset of depression, and not with the symptoms of depression (American Psychiatric

Association, 2000).

Insomnia often has a fairly sudden onset caused by psychological, social, or medical stress. Though, in some

cases, it may develop gradually and without a clear stressor. Insomnia is characterized by sleep deprivation,

and associated with increased physiological, cognitive, or emotional arousal in combination with negative

conditioning for sleep (American Psychiatric Association, 2000).

stress-related psychiatric disorders. Although not an

exhaustive list, this overview illustrates how differ-

ent aspects of stress can explain different disorders.

For example, depression and insomnia have a strong

appraisal component, whereas PTSD is mainly ex-

plained by a severe stressor. Moreover, Table 1 high-

lights the temporal course of the stressor as well as

the stress response. Although the actual stressor can

be both acute and chronic, there is a chronic stress

response for all diseases; either at the onset (e.g., de-

pression) or as a symptom (e.g., PTSD).

In general, the diagnosis of stress-related psychi-

atric disorders is, amongst other methods, performed

with a careful interview (American Psychiatric Asso-

ciation, 2000). During this interview, the clinician

has to determine if the patient suffers from exces-

sive stress. Moreover, the clinician has to identify the

possible stressor causing an excessive stress response.

Hence, a key diagnostic task is to determine whether

or not the patient suffers from excessive stress in re-

lation to speciﬁc stressors.

The diagnosis of excessive stress is repeated dur-

ing treatment as well, in order to indicate the progress

of the treatment. Depending on the treatment type,

this diagnostic repetition can even be part of the

treatment itself. (Everly, Jr. and Lating, 2002)

differentiate between three therapeutic genres: 1)

avoid/minimize/modify stressors; 2) reduce excessive

arousal and organ dysfunction; and 3) ventilate or

express the stress response. The latter incorporates

a repeated expression of the stress response and re-

quires a repeated measurement of it; for example, as

is done with the treatment of PTSD. Technology is

beginning to play a more signiﬁcant role in the treat-

ment of stress disorders, evidenced by a new treat-

ment method of self-help and minimal contact thera-

pies which has proven to be succesful for certain types

of patients (Newman et al., 2010).

Currently, the measurement of excessive stress

is problematic. A clinician uses diagnostic criteria

based on a range of questionnaires to support this aim.

Introspection and the expert opinion of the clinician

are at the basis of these tools. Inherently, subjective

measures can be unreliable. Moreover, these ques-

tionnaires can be a burden for the patient.

The aim of the study is to lay the foundation for

an Artiﬁcial Therapy Assistant (ATA), capable of as-

sisting therapists through an objective measurement

of stress. Moreover, such a system can be useful

for minimal-contact or self-help interventions as well

(Newman et al., 2010). The next section will identify

the prerequisites that had to be taken into account for

this system. After that, in Section 3, a clinical study of

objective stress measurement will be introduced. This

study involved the participation of patients suffering

from a PTSD, which enabled the salient determina-

tion of stress characteristics and, with that, an indica-

tor of stress. Section 4 presents the results obtained

through this study and deﬁnes a model that can serve

as the foundation for an ATA. Finally, in Section 5 we

discuss the contribution of this work to the diagnosis

and treatment of stress disorders.

HEALTHINF 2011 - International Conference on Health Informatics

358

2 CONSIDERATIONS

AND SPECIFICATIONS

The aim of the study was an agent that is able to sup-

port psychiatrists and psychologists in their diagnosis

of excessive stress. Key to this system is the mea-

surement of signals indicative of emotions, in partic-

ular of stress, and the determination of intensity. It is

the intensity that can help the therapist in determining

whether or not the patient is suffering from an exces-

sive stress response. As such, the agent aims to sup-

port the decision of the clinician, contrary to giving a

decision itself.

It is known from literature that multiple physi-

cal sources can be applied as stress indicator (Lader,

1975; van den Broek et al., 2010). This research

elaborates on speech, which has a number of advan-

tages: i) In therapy sessions, speech is often already

recorded. Hence, using speech requires no additional

effort for the therapists; ii) Speech processing is fully

unobstrusive; and iii) There is fairly little noise in the

speech signal, as therapy sessions are generally held

in a controlled environment.

2.1 Feature Extraction

For the following features there is a fair amount of

support for their affective information: pitch, energy,

high-frequency energy, and to a lesser extent zero-

crossings rate (Kedem, 1986; Scherer, 2003; El Ayadi

et al., xxxx). Although there is no general concen-

sus on the best features for stress detection, there is

substantial evidence for these. Hence, they extracted

from the audio signal.

For a domain [0,T ], consisting of N number of

samples, the energy of the speech signal is deﬁned as:

20 log

N−1

∑

n=0

(n), (1)

where the amplitude or sound pressure of the signal

is denoted in Pascals (Pa) as x(n) and the auditory

threshold P

is 2 ·10

−5

Pa (Boersma and Weenink,

2006). The energy of the speech signal is also de-

scribed as the Sound Pressure Level (SPL). It is

expressed in decibels (dB) relative to the auditory

threshold P

; i.e., in dB (SPL).

To extract speech’s high-frequency energy (i.e.,

the energy for the domain [1000,∞] in Hz), the sig-

nal ﬁrst has to be transformed to the frequency do-

main (Banse and Scherer, 1996). This is done by a

fast Fourier implementation of the discrete Fourier

transform. The discrete Fourier transform (Lyons,

2004):

X(m) =

N−1

∑

n=0

x(n)e

−j2πnm/N

, (2)

with j representing the

√

−1 operator and where m

relates to frequency by f (m) = m f

/N. Here, f

the sample frequency and N is the number of bins.

The number of bins typically amounts to the next

power of 2 for the number of samples being ana-

lyzed; e.g., 2048 for a window of 40 msec. sampled

at 44100 Hz. The energy for the domain [M

where f (M

) = 1000Hz and f (M

) = f

/2 (i.e., the

Nyquist frequency), is deﬁned by:

20 log

−M

∑

m=M

|X|

(m). (3)

The F0 or pitch is extracted using the autocor-

relation method. The autocorrelation is the cross-

correlation of the signal with itself, where the cross-

correlation denotes the similarity between two signals

as a function of a time-lag between them. The auto-

correlation R of signal x at time lag m is deﬁned as:

(m) =

N−m−1

∑

n=0

x(n +m)x(n) (4)

where N is the length of the signal. The autocorrela-

tion is then computed for each time lag m over the do-

main M

= 0 and M

= N −1. The global maximum

of this method is at lag 0. The local maximum beyond

0, lag m

max

, represents the fundamental frequency, if

its normalized local maximum R

max

)/R

(0) (its

harmonic strength) is large enough (e.g., above .45).

The fundamental frequency is derived by 1/m

max

. We

refer to (Boersma, 1993) for a detailed description of

the (implementation of) the F0 extraction.

The zero crossings rate of the speech signal is also

computed. This is deﬁned as:

N−1

∑

n=1

{

x(n)x(n −1) < 0

}

, (5)

where N is the number of samples of the signal ampli-

tude x. The I

{

}

serves as a logical function (Kedem,

1986).

3 CLINICAL STUDY

The exact relation between the identiﬁed features of

speech and stress is as yet unclear. Two problems

make it hard to compare most previous studies and

methods on stress detection. First, many studies use

mimicked emotions instead of true emotions as the

TOWARDS AN ARTIFICIAL THERAPY ASSISTANT - Measuring Excessive Stress from Speech

359

basis of their model of stress (i.e., acted vs. expe-

rienced emotions). Second, since there is often no

ground truth, it is unclear if the measured vocal pa-

rameters represent an (induced) affective state. For

more information on these problems, see (Scherer,

2003). To arrive at an acoustic stress indicator, this

section presents a study of stress in speech, dealing

with the two identiﬁed problems.

The study consisted of two phases, triggering ei-

ther a happy or an anxious state in the patients. Hence,

anxiety was the stressful emotion chosen to induce

stress. The order of sessions was counterbalanced

over the participants. 25 Female PTSD patients (mean

age: 38) participated voluntarily. An informed con-

sent was signed by all participants. Having PTSD pa-

tients as participants had several advantages. First,

PTSD patients are relatively sensitive to stress and,

thus, to stressors. Hence, they were expected to react

more intensively to the emotion elicitation. Second,

within the context of this study, the use of real pa-

tients increases the ecological validity.

The patients had to read two stories aloud, one of

an anxious and one of a happy situation. To prevent

any interfering factors the stories were kept similar on

their syntactic structure and their complexity. More-

over, the order of the stories was counterbalanced over

all participants. Before the patients started with the

emotion inducing stories, they read a sample story to

familiarize themselves with the task.

The stories served as emotional Stroop tasks

(Williams et al., 1996), since they included words

that induced either anxiety or a happy emotional state.

Emotional Stroop tasks are frequently used in clinical

psychology and psychiatry research and are accepted

as a reliable method for eliciting emotions. Emo-

tional Stroop tasks can be deﬁned as the presentation

of stimuli that are expected to evoke emotions, due to

an attentional bias of the participants. In this research,

the Stroop effect was achieved through anxiety trig-

gering words incorporated in one of the stories.

To be able to derive stress from speech, several

steps had to be performed. First, the signal was

recorded. This was done using a standard PC, a

microphone preampliﬁer, and a microphone. The

recording’s sample rate was 44.1 kHz, mono chan-

nel, and a resolution of 16 bits. The recordings were

divided into samples of approximately one minute of

speech. This resulted in a one-on-one mapping be-

tween the ground truth (explained furtheron) and the

speech features. Second, other voices and speckle

noise were removed from the recorded signal.

Several features were extracted from the clean sig-

nal; see also Section 2.1. From each of these fea-

tures, a number of statistical parameters were de-

1 2 3

SUD

Time (minutes)

Anxiety

Happy

Figure 1: Reported stress per group and time.

rived: mean, median, standard deviation (std), vari-

ance (var), minimum value (min), maximum value

(max), range (max −min), the quantiles at 10%(q10),

90%(q90), 25%(q25), and 75%(q75), the inter-

quantile-range 10%−90% (iqr10, q90 −q10), and the

inter-quantile-range 25% −75% (iqr25,q75 − q25).

All features were computed using a time window of

40 msec. and a step length of 10 msec.; i.e., every 10

msec. over the next 40 msec. of the signal. Next, the

statistical parameters were derived over time chunks

of 60 sec., allowing a one-on-one comparison with the

Subjective Unit of Distress (SUD) data.

To enable the validation of the speech parame-

ters they were compared to a subjective measurement:

the SUD. The SUD is a Likert scale indicative of the

(dis)stress a participant experiences at the moment of

measurement. For this study, a linear scale with range

0 to 10 was used on which the participants were in-

structed to place a cross or a dot. After (Wolpe, 1958)

introduced the SUD, it has proven itself to be a re-

liable measure of one’s emotional state. The partici-

pants used the SUD every minute, making it a routine

task. The SUD was used as ground truth for the de-

rived speech parameters (See Section 2.1).

4 RESULTS

If the manipulation of stress was successful, the re-

sults can be used as a stress indicator. Figure 1 il-

lustrates the mean values of both manipulations and

denotes the conﬁdence intervals without the inter-

subject variance (Cousineau, 2005) through the ver-

tical bars. This ﬁgure shows that the manipulation

has been successful. Furthermore, when isolating the

anxiety condition, a trend was visible for time on

SUD scores (F(2,56) = 2.726; p = .07).

In order to create a generic stress indicator, the

most relevant of all features and accompanying pa-

HEALTHINF 2011 - International Conference on Health Informatics

360

rameters were selected. This selection process was

done with a linear regression model (M ). A M ex-

plains how p independent variables (predictors, x)

predict dependent variable y. In order to do so, p

optimal weighting factors Beta (B) over each of the

i = 1,...,n observations are determined:

= B

+ B

+ ···+ B

+ ε

, (6)

where ε

represents unobserved random noise. The

method used to determine the average optimal

weighting factors over all n observations is the ordinal

least squares method. To reduce the number of pre-

dictors, a backward selection algorithm was applied.

Through an iterative process, this algorithm removes

the non-signiﬁcant predictors (p > .10) for subjective

stress. As the backward method uses the relative con-

tribution to the model as selection criterium, the in-

terdependency of the features is taken into account

as well (Harrell, Jr., 2001). This makes it a robust

method for feature and parameter selection.

The model was created using the SUD scores

of the anxiety and happy conditions (See Figure 1).

Here, a M containing all features and all parame-

ters (i.e., in total 40 predictors), explained 69.72% of

the variance: R

= .697 and R

= .575, F(40,99) =

5.70, p < .001. Applying the backward selection

method with 22 iterations, leaving 18 predictors, the

model still explained 67.37% of the variance: R

.674 and R

= .625, F(18,121) = 13.88, p < .001.

The model and the used features are described in more

detail in (van der Sluis et al., 2010).

5 DISCUSSION

25 Patients reported stress that had successfully been

caused by reading/telling a carefully created story.

By comparing speech features to a subjective report

of stress, this study deﬁned and evaluated an acous-

tic proﬁle of stress characteristics in speech. The

acoustic proﬁle was shown to explain nearly 70% of

variance in the subjectively reported stress. Hence,

demonstrating the feasability of speech as an objec-

tive measure of experienced stress and, with that, as

an ATA.

Although it is only one of many ways to induce

emotions, storytelling was shown to be particularly

useful in creating an emotion-induced speech signal.

In particular, it is likely to create true emotions, this

contrary to many other commonly used methods. The

triangulation of the SUD and various speech charac-

teristics suggests that indeed true emotions were trig-

gered through the storytelling.

A potential problem with the acoustic stress indi-

cator, as introduced, is described by the existing theo-

retical distinction of emotional and emotive commu-

nication (Cafﬁ and Janney, 1994). Emotional com-

munication is a type of spontaneous, unintentional

leakage or bursting out of emotion in speech, while

emotive communication has no automatic or neces-

sary relation to “real” inner affective states. Emotive

communication is a strategic signaling of affective in-

formation in speaking to interaction partners that is

widespread in interactions; see also (Cafﬁ and Janney,

1994). It uses signal patterns that differ strongly from

spontaneous, emotional expressions and can be both

intentionally and unintentionally accessed (Banse and

Scherer, 1996).

Another issue is the distinction between cognitive

and emotional stress, which is known as the problem

of emotion speciﬁcity (Zeelenberg et al., 2008). Emo-

tion speciﬁcity distinguishes cognitive stress, the in-

formation processing load placed on the human op-

erator while performing a particular task, and emo-

tional stress, the psychological and physiological ten-

sion due to emotions triggered before or during the

task.

In general, the subjectively reported stress was

somewhat dispersed. This is likely to be partly due

to inter-personal differences and, consequently, indi-

cates that the stories did have an inﬂuence. More-

over, a trend was shown for the anxiety inducing story

to create stress over time, supporting this inﬂuence.

These results suggest the value as well as the draw-

backs of storytelling. Two problems can be identiﬁed:

• inducing an affective state with stories is strongly

dependent on the temporal course; i.e., a story

needs a build-up; and

• there were substantial inter-personal differences

in the experience of the stories.

The latter problem may be useful for diagnostic goals.

Inter-personal differences are likely to be caused by

differences in appraisal. Hence, this can be used

to assess a patient’s appraisal patterns, which have

been identiﬁed as a major component for certain psy-

chiatric illnesses; for example, depression (Kessler,

1997).

The explained variance of 70% can be considered

as high, especially considering the number of partici-

pants. Moreover, since the model is not personalized,

some generic chacteristics of stress in speech seem to

be uncovered. However, some restrictions also apply:

• only PTSD patients participated, while other

patient groups might show different stress re-

sponses;

TOWARDS AN ARTIFICIAL THERAPY ASSISTANT - Measuring Excessive Stress from Speech

361

• many stressful emotions have been identiﬁed,

these may be different kinds of stress; and

• restrictions applying to storytelling for emotion

elicitation may have inﬂuenced the results.

These three restrictions can be seen as future research

challenges. Namely, to use other patient groups, emo-

tions, and emotion elicitation techniques.

Using the acoustic proﬁle, one can arrive at an

ATA for the diagnosis and treatment of stress-related

psychiatric disorders. An ATA can help the clinical

setting in several ways, to:

1. support the measurement of stress responses;

2. give decision support on whether a patient suffers

from excessive stress;

3. aid the treatment of stress disorders; and

4. improve self-help and minimal-contact therapy

methods (Newman et al., 2010).

Through making the measurement objective, the mea-

surement of stress becomes more reliable; i.e., no

longer solely relying on introspection. Objective mea-

surement also increases inter- and intra-expert relia-

bility. Moreover, diagnosis, decision-making in gen-

eral, and treatment could become more ﬁne-grained.

Concluding, an important and signiﬁcant step to-

wards an ATA for stress-related psychiatric disorders

has been made. This study has shown that an objec-

tive measurement of stress through speech is feasible.

Par excellence, the feasibility of objective stress mea-

surement illustrates the possibility of more objective

measures for the generally subjective ﬁelds of psy-

chology and psychiatry.

ACKNOWLEDGEMENTS

We gratefully acknowledge the PTSD patients for vol-

untarily participating in this research. We thank Lynn

Packwood for proof reading this article.

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