of similar vocal elements as a crucial acoustic structure in human laughte

Dietmar Todt

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Series of similar vocal elements as a crucial acoustic structure in human 
laughter 
Silke Kipper & Dietmar Todt 
Institute of Biology: Animal Behaviour Group, Free University Berlin 
silkip@zedat.fu-berlin.de, todt@zedat.fu-berlin.de 
ABSTRACT 
Among  the  many  variable  sounds  in  human 
laughter, vocalizations often contain series of vocal 
elements of similar acoustic properties. This study 
aims  to  elucidate  whether  such  element  series 
contain  trajectories  of  changes  in  acoustic 
parameters  that  might  be  used  to  encode 
information, e.g. on the state of the signaller. We 
recorded bouts of laughter of adult humans (N = 
17) and used a multi-parametric sound analysis to 
describe the acoustic parameters of vocal elements 
and  their  variation.  We  could  show  that  these 
elements are distinguishable between individuals, 
but not necessary between female and male voices. 
We suggest that the series of similar elements with 
gradients in acoustic changes within laughter bouts 
might  account  for  the  stereotype  and  therefore 
predictable impression of laughter vocalizations.  
Keywords:  laughter  vocalization,  acoustic 
signaling, multi-parametric sound analysis.  
ZUSAMMENFASSUNG 
Innerhalb  von  Lachvokalisationen  lassen  sich 
Serien  von  Elementen  mit  ähnlichen  akustischen 
Eigenschaften  charakterisieren.  Wir  untersuchten, 
ob  sich  innerhalb  solcher  Elementfolgen 
Trajektorien  akustischer  Parameter-Änderungen 
beschreiben  lassen,  die  zur  Kodierung  von 
Information genutzt werden können. Lachepisoden 
von  17  Erwachsenen  wurden  in  einem  multi-
parametrischen  Verfahren  analysiert,  um 
akustische Parameter von Elementen sowie deren 
Variabilität  zu  beschreiben.  Die  Elemente  ließen 
sich anhand ihrer Eigenschaften den verschiedenen 
lachenden Personen zuordnen, das Geschlecht des 
Lachers  war  jedoch  nicht  in  jedem  Fall  zu 
dekodieren.  Wir  schlagen  vor,  dass  Serien 
ähnlicher  Elemente  mit  geringen  Veränderungen 
von  Element  zu  Element  sowie  bestimmte 
Gradienten  solcher  Veränderungen  den 
vorhersagbaren  Höreindruck  des  Lachens 
hervorrufen.  
1.  INTRODUCTION 
Some  nonverbal  human  vocalizations  such  as 
infant  crying  or  laughter  contain  series  of 
acoustically similar elements [2,6,14]. It has been 
suggested  that  within  such  series,  gradients  of 
parameter  changes  might  encode  higher-order 
information that adds to the information encoded 
in  the  element  structure  [12].  Playback 
experiments  using  laughter  recorded  in  natural 
settings  or  experimentally  modified  laughter  did 
provide  evidence  that  not  only  element 
characteristics, but also parameter changes within a 
series affect the evaluation of laughter by listeners 
[1,4,5,7,11]. 
The acoustic characteristics of human laughter 
have been investigated in several studies that either 
emphasized the stereotypy of the signal [8,9] or in 
contrary  the  enormous  acoustic  variability  of 
laughter vocalizations [2,13]. Here, we investigate 
acoustic  variation  in  laughter  vocalizations  by 
means of a multi-parametric sound analysis [10]. 
Comparing acoustic parameters of vocal elements 
for corresponding positions in a series will allow 
us  to  investigate  the  variability  of  laughter 
elements  as  well  as  rules  underlying  parameter 
changes within series of laughter elements. 
2.  METHODS 
2.1.  Recordings and acoustic analysis 
We  recorded  spontaneous,  unforced  laughter 
during a reading task in which participants heard 
themselves  with  a  short  time  delay  (200  ms) 
through  headphones  (speech  delayer:  SPVZ, 
Geräte  Zak,  Simbach,  Germany).  This  procedure 
led  to  problems  in  articulation  which  readily 
resulted  in  bursts  of  laughter  (delay  in  playback 
was  interrupted  while  recording  laughter).  17 
people volunteered to participate in the study (10 
males, 7 females, mean age 36.6 ± 8.1 years). For 
recordings we used a DAT recorder (Sony TCD-
Interdisciplinary Workshop on The Phonetics of Laughter, Saarbr¨ ucken, 4-5 August 2007 3D10)  connected  to  a  Sennheiser  ME  62 
microphone.  
Any laughter response that was comprised of a 
sequence of at least three elements was included in 
the  analysis  (overall,  178  bouts  of  laughter  with 
1515 elements). The acoustic properties of laughter 
vocalizations  were  analyzed  using  the  sound 
analysis program Avisoft-SASLab Pro. R. Specht, 
Berlin (16-bit  resolution, sampling  rate  16  kHz). 
As  a  laughter  element,  we  defined  each  discrete 
sound pattern that was not interrupted by pauses 
longer than 10 ms. Each laughter bout consisted of 
a  number  of  elements  within  a  wide  range  of 
acoustic characteristics and was finished either by 
a sound produced during inspiration or by the onset 
of  speech.  Laughter  bouts  often  contained 
successions of similar elements. To operationally 
define  these  series,  the  following  criteria  was 
applied: homotype element series were all element 
successions  where  successive  elements  did  not 
show more than 50 % difference in at least two of 
three  acoustic  parameters (duration, interval,  and 
F0-max, see Fig. 1 for illustration). 
Figure 1: Spectrogram of a laughter vocalization (8 
kHz, 16-bit). Acoustic structures are named below. 
 
For  each  bout  of  laughter  we  measured  the 
duration of the whole bout, of the series within the 
bout, and of each element, and for the latter, also 
the maximum value of the fundamental frequency. 
In addition, we obtained several measures for each 
element  (‘multi-parametric  sound  analysis’)  [10] 
by the following procedure: We digitized elements 
(8 kHz sample rate), and calculated spectrograms 
using a Fast Fourier Transformation (window size 
512 sample points, overlap 93.75, time resolution 2 
ms).  Using  the  analysis  program  ConAn  0.9  (R. 
Mundry, Berlin), these spectrograms were used to 
calculate 115 measures on frequency and energy 
distribution  over  time  and  on  the  shape  of  the 
fundamental frequency for each element.  
2.2.  Data analysis and statistics 
A  discriminant  function  analysis  (DFA)  was 
applied to detect differences in laughter elements 
according to the two factors speaker identity and 
gender.  In  both  analyses  (speaker  and  gender), 
discriminant functions were calculated only on a 
subset of elements (internal elements, int.) whereas 
all  elements  were  classified  (external  elements, 
ext., ‘hold-out-sample method’ [3]). Analyses were 
conducted  with  7  acoustic  parameters  (statistic-
based  selection  out  of  the  115  parameters 
measured), 14 subjects (7 male, 7 female), and 8 
laughter series per subject. Loadings of parameters 
on the discriminant function were used to estimate 
their  contribution  to  the  discrimination  of 
investigated classes.  
All  differences  between  groups  or  parameters 
were tested with non-parametric statistic tests. 
To  uncover  trajectories  of  parameter  changes 
within a laughter series, for some of the acoustic 
parameters measured, we calculated such changes 
according  to  the  element’s  position  within  the 
series  and  introduced  an  additional  measure 
(changes  of  10  %  or  more)  in  order  to  roughly 
reflect  perceptual  abilities  in  humans.  To  assure 
comparability  of  results,  this  measure  was  only 
applied to series containing at least six elements. 
3.  RESULTS 
3.1.  Structure of laughter bouts 
Laughter bouts were typically initiated by one or 
two  ‘singular’  elements  (i.e.  non-repeated,  with 
large  variability  in  acoustic  parameters).  These 
were often followed by a succession of elements 
with predictable similarity, i.e. a homotype series. 
After  this  homotype  series  sometimes  more 
singular elements followed and in the end often a 
sound  produced  during  inspiration.  Of  all  bouts 
analyzed,  only  9  bouts  (5%)  produced  by  7 
different participants did not contain a homotype 
series.  On  the  other  hand,  never  did  a  bout  of 
laughter contain more than one series. 
In  the  majority  of  cases,  homotype  laughter 
series  constituted  the  longest  part  of  a  laughter 
bout  (Fig.  2).  Same  significant  results  were 
obtained  by  comparing  the  duration  of  temporal 
structures  of  the  series  for  each  participant, 
separately.  
elements
series
bout of laughter
sec
Interdisciplinary Workshop on The Phonetics of Laughter, Saarbr¨ ucken, 4-5 August 2007 4Figure 2: Temporal pattern of laughter bouts: series 
contribute most to a bout (Friedman one-way ANOVA 
for mean dur/subject, N=17, 
￿2=34.0, p < 0.001). 
 
Duration’s of homotype laughter series did not 
differ between males and females, but we found 
considerable  differences  with respect  to  different 
participants. 
3.2.  Characterization of laughter elements in 
homotype series 
The  average  rate  of  elements  produced  within 
series was 4.7 ± 0.8 /s. This measure did not differ 
between  males  and  females,  but  between 
individuals,  again.  The  results  of  the  DFA  also 
confirmed that individuality, but not gender, was 
distinguishable  by  the  acoustic  parameters  of 
laughter elements (Table 1).  
Table 1: Results of the discriminant function analyses 
and Binomial tests. Data were balanced by randomly 
selecting equal numbers (N=7) of subjects for genders. 
Within  each  analysis,  subjects  contributed  equal 
numbers of internal elements. 
  Subset  Correct 
classified (%) 
Elements  p 
int.  47.3  112  < .001  Subject 
ext.  19  807  < .001 
int.  59  112   .01  Gender 
ext.  56  807   .07 
 
Thus,  laughter  elements  within  series  contained 
acoustic characteristics that, in their combination, 
made it possible to distinguish between laughing 
people.  Parameters  that  were  especially  decisive 
for differences between subjects (i.e. such loading 
high on DF1) were those describing the frequency 
contour  within  an  element:  a  measure  of  the 
fundamental frequency (FMedS), the slope of the 
element  (as  a  measure  of  frequency  contour, 
SlStEnd),  the  time  until  the  minimal  slope  was 
reached  (LocSlMin),  and  the  maximal  slope 
(SlMax). 
3.3.  Gradients  of  parameter  changes  within 
laughter series 
For  seven  parameters  (four  that  most  effectively 
characterized individual differences and three that 
were  efficient  in  eliciting  different  responses  in 
playback  experiments  [4]),  we  correlated  the 
measures of these parameters with the position of 
the  elements  in  a  series  in  order  to  describe 
gradients  within  acoustic  parameters.  We 
calculated  Pearson’s  correlation  coefficients  for 
each  parameter  and  each  series,  separately  (all 
N=98, balanced for speaker and gender). Only the 
distribution of correlation coefficients for duration, 
interval, and fundamental frequency differed from 
a normal distribution, thereby pointing to gradients 
of parameter changes (Kolmogorov-Smirnoff-tests, 
duration:  Z=1.47,  p=0.03,  interval:  Z=2.60, 
p=0.001, fundamental frequency Z= 2.60, p=0.001, 
FMedS=1.37, p=0.05, SlStEnd, LocSlMin, SlMax: 
all Z<0.095, all p n.s.). 
Correlation coefficients tended to be positive for 
intervals  (22  positive  out  of  24  series  with  a 
significant  correlation),  whereas  duration  was 
clearly negatively correlated with element position 
(40  negative  out  of  46  series  with  a  significant 
correlation).  Element  frequency  within homotype 
series  either  decreased  or  increased  (37  negative 
out of 48 significant correlation’s). In other words: 
whereas intervals tended to get longer towards the 
end of a series, duration’s declined in the course of 
a  series.  The  fundamental  frequency  did  show 
either  decreasing  or  increasing  gradients  in 
different series.  
The characterization of gradients by means of 
differences  of  at  least  10  %  between  successive 
elements specified these results. Gradients showed 
a characteristic distribution over the course of the 
series (Fig. 3).  
For the duration, increases did occur less often 
towards the end, whereas decreases tended to occur 
more often in the later transitions of a series. The 
interval was in most of the cases increasing within 
the  course  of  a  series.  Pitch  showed  only  few 
element transitions with differences of above 10 %. 
Increases  of  pitch  occurred  more  often  in  the 
0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5 5.5 s
M 01
M 02
M 03
M 04
M 05
M 06
M 07
M 08
M 09
M 10
F 01
F 02
F 03
F 04
F 05
F 06
F 07
Laughter start
Laughter series
Laughter end
(12)
(11)
(10)
(8)
(16)
(11)
(8)
(10)
(19)
(11)
(10)
(5)
(10)
(17)
(6)
(9)
(5)
Duration
P
a
r
t
i
c
i
p
a
n
t
s
Interdisciplinary Workshop on The Phonetics of Laughter, Saarbr¨ ucken, 4-5 August 2007 5beginning  of  series,  whereas  decreases  occurred 
more often towards the end of a series. 
 
 
 
 
Figure  3:  Proportion  of  gradients  (increase,  stable, 
decrease)  in  the  element  transitions  of  laughter  by 
means of at-least-10-% differences between successive 
elements. 
 
4.  DISCUSSION 
Inquiries  into  the  structure  of  laughter  elements 
and the dynamics of parameter changes in human 
laughter  allowed  us  to  show  that  this  display 
includes  sequences  of  similar  vocal  elements. 
Within  such  ‘homotype  series’,  acoustic 
parameters  did  only  exhibit  small  changes  from 
element  to  element.  The  comparison  of  acoustic 
features  of  successive  elements  uncovered  some 
typical  trajectories  of  parameter  changes  within 
laughter series. For example, there was a tendency 
for the duration of elements to decline within the 
series and for intervals to increase.  
The  range  of  parameter  variability  within  the 
series  of  laughter  elements  is  individually 
different,  with  measures  characterizing  the 
frequency  shape  of  an  element  being  especially 
decisive  between  subjects.  Interestingly,  neither 
these  nor  other  investigated  parameters  showed 
systematic  differences  between  female  and  male 
subjects. The failure to find such differences in our 
study  might,  on  the  one  hand,  be  explained  by 
methodological constraints. For example, whereas 
in  other  studies  all  laughter  vocalizations 
performed during a whole bout or burst of laughter 
were  analyzed,  we  considered  only  homotype 
laughter  elements.  On  the  other  hand,  listeners 
evaluating  laughter  sometimes  did  report 
difficulties to discriminate female and male voice 
characteristics (Kipper, unpublished data). Such a 
reduction of acoustic differences between male and 
female  voices  might  point  to  the  biological 
significance of laughter. There is a consensus that 
laughter is used as an ‘in-group-signal’ that serves 
to generate and maintain social bonds [e.g. 1,6,7]. 
A  signal  serving  such  a  function  might  not  be 
designed to accent differences between sub-groups. 
We  were  able  to  extract  specific  rules  of 
parameter variation within laughter series as given 
if parameter values shift from element to element 
within a sequence. Such rules, forming gradients or 
trajectories,  have  been  documented  in 
communicative systems of many animals and have 
been  argued  to  serve  communicative  functions 
there [12]. 
5.  CONCLUSION 
In  the  present  study  we  investigated  the 
organization of laughter vocalizations applying the 
conceptual framework of homotype signal series. 
This  allowed  us  to  explain  several  features  of 
human laughter and to extract rules of parameter 
variation.  Studies  on  laughter  vocalizations  in 
different social settings are crucial to verify these 
results. At the same time such investigations will 
be the only way to raise our understanding on the 
signal  design  and  variability  of  human  laughter. 
These  studies  should  include  either  side  of  the 
signaler/recipient  system  and  consider  the 
production  and  performance  as  well  as  the 
perception and evaluation of human laughter. 
 
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[2]  Bachorowski,  J.-A.,  Smoski,  M.J.,  Owren,  M.J.  2001. 
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duration
0
20
40
60
80
100
1-2 2-3 3-4 4-5 5-6
Increase
 Stable
Decrease
%
interval
0
20
40
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1-2 2-3 3-4 4-5 5-6
%
pitch
0
20
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element transitions
%
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