Acoustic analysis of intentional vocalizations by
autistic and non-autistic monozygotic twins.
Claire Scoular
Supervisor: Jonathan Delafield-Butt
MSc Psychological Research Methods
The University of Edinburgh
2008
1
Statement
This statement is to confirm that all the work presented in this document is my
own work.
Claire Scoular
2
Contents
Abstract............................................................................... 4
Introduction......................................................................... 5
Methods and
Materials.............................................................................. 15
Results..................................................................................19
Discussion............................................................................ 31
Appendices........................................................................... 44
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Abstract
This study focuses on early development communication to help determine autism at
an earlier stage. Infant cries have frequently been studied and analysed as a reliable and
interesting form of early communication (Corwin, Lester, and Golub, 1996).This study
examine the cries of a young infant who is later diagnosed with autism. Her cries will be
compared with those of her identical twin sister who does not later get diagnosed with autism.
Trevarthen and Daniel (2005) have used home video tapes to analyse dynamic and timing
elements the monozygotic twins, one of which later developed autism while the other
developed normally. Their research found differences in the children’s movement and
interactions. In particular, the twin developing autism exhibited a lack of purposeful
movement suggesting a core deficit in prospective control. Further studies have concluded
that there are differences in the vocal movements of children with autism.
A novel acoustic analysis designed by the University of Edinburgh’s Perception in
Action laboratory was used to examine the cries of the twins. The analysis is based on the
General Tau Theory of movement which claims that all movements are perceptually and
intrinsically guided by an internal Tau guide. Successful movement is based on the
simultaneous closing of motion gaps. If unsuccessful their prospective control is weak. It is
suggested that autistic sufferers may not possess this internal intrinsic guide and it may
explain their motor difficulties.
The Tau coupling percentages (%), pitch (T), amplitude (A) and timbre (K) of the
twins’ cries was examined to determine any differences between the two. Many significant
differences within these variables were found between the twins. This study sets the
foundations for further, more in depth research in this area. The focus of the study was a
small sample and very focused but it simply aims to highlight a possible route into further
studying the area of autism at an early age in order to detect signs of autism earlier.
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Literature Review
Autism Background
Autism is a neuro-developmental disorder that produces difficulties in socialising,
communicating and displays of repetitive behaviour Autism was initially described by
Kanner (1943) as a triad of impairments. He claimed that autism manifests itself as specific
behavioural signs, and impairments in socialising and communicating. Autism is presented
on a spectrum and all individuals vary in capabilities depending on their intellect and social
development as an individual. A lack of social and communication skills are amongst the
earliest and most common signs of autism (Osterling & Dawson, 2006). Children with autism
generally don’t attend to others often or share attention with them (Entremont & Yazbek,
2006). Autistic children also find it difficult to understand the mental states of others (BaronCohen, 2006). One theory that may explain these and other deficits in autism is the ‘theory of
mind’. Also known as ‘Mind blindness’ it is believed that autistic sufferers do not possess
this theory of mind which makes it difficult for them to interpret and interact with their
environment and the people in it. It involves difficulties in understanding others thoughts and
actions from their prospective, their intentions and how their own actions affect others
(Baron-Cohen, Leslie & Frith, 1995). Without this notion that other people have separate
thinking it is hard for an autistic child to understand the intentions of others. This was
confirmed in a study by Entremont and Yazbek (2006) who found that autistic children do
not show an understanding or appreciation of the intentions of others. Another study by
Pierno et al. (2006) found that autistic children fail to correctly interpret another person’s
actions or gaze leading to a failing in planning their next action. These difficulties in
comparison to normally developing children will produce a lack of understanding of their
own and others motor intentions (Pierno et al., 2006). If an autistic child struggles to define
and understand the intentions of their actions they may struggle with controlling their actions.
Glazebrook, Elliott and Szatmari (2008) found that autistic individuals used advance
information to plan their movements when the information was direct. However, their actions
were less controlled when their strategies for movement were self-generated.
Autism presents itself as a neurological developmental disorder. The precise
mechanisms involved in autism are still not completely understood but it is believed to be
related to abnormal neurotransmission and specific brain regions that mediate motor control
5
such as the cerebellum and sub cortical white matter (Bauman & Kemper, 2005;
Courchehesne, 2002). This may potentially affect motor performance. Every autistic will
have different deficits and their severities in each of these will vary. Although these
impairments are known, there is no single reliable measure found that diagnoses autism. All
of the existing measurements draw on Kanner’s (1943) triad and none of them focus on the
early development of children, particular in their first year of life. Typically children are not
diagnosed until around 3 years old despite many claiming that there are evidential signs long
before this age (Werner et al., 2000). Early diagnosis is always preferable for any conditions
so that intervention can be applied so alternative methods have tested to determine symptoms
of autism earlier.
General Tau Theory
In order to move successfully we humans and animals need to successfully control
their movements. One theory on movement is General Tau Theory. General Tau theory aims
to explain how movement is perceptually and intrinsically guided which involves the closure
of gaps in motion and the use of sense organs to reach the desired end goal (Lee, 2005).
Adequate control of a movement is prospective, suggesting it is constantly based on sensory
information that can be accessed at some point in the future. The internal intrinsic guide
enables people to gain prospective control of their movements. Lee (2005), states that ‘an
adequate theory must explain how movements are perceptually and intrinsically guided. It
must explain the form of the guiding perceptual information that enables prospective
guidance of movement. And it must be biologically plausible.’ It is unclear where this
intrinsic Tau guide originates from but suggestions centre on the brain’s energy flows and the
patterns of the nervous system (Lee, 1998). Lee bases his theory on the motion gap which
represents the movement between the current state and the end goal of an action (1999). All
movements can be described by the closure of a motion gap and Tau represents the time it
takes for a motion gap to close. For an action to be completed successfully motion gaps need
to close simultaneously, this is called Tau coupling.
Tau coupling is when two Tau remain in constant proportion over a period of time.
Motion gaps in movement need to be simultaneous when closing in order to have a successful
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movement. Simultaneous closure of the motion gap and therefore a successful movement is
represented by a high tau coupling percentage of >90%. A disproportion in Tau can result in
an unsuccessful movement and are represented by a lower percentage. Higher percentage
couplings represent more successful movements.
Analysis based on general Tau theory has been conducted many times before on
many different species. Studies have been conducted on the suckling of neo-natal babies
(Craig and Lee, 1999; see Lee, 2005). Human running movements have been studied as well
as the landing movements of bats and pigeons (see Lee, 2005). Research has been found to
suggest people who have movement difficulties such as Parkinson’s disease do so as they
lack to ability to use this intrinsic Tau guide and therefore, present low tau coupling
percentages (<90%) (Lee et al., 1999). Without this guide they find it difficult to couple their
movement to anything and as a result have difficulty in self-generated movement. This could
also explain the difficulties many autistics face with physical and vocal movements. This
intrinsic Tau guide may be left out of autistics in development which could explain their
differences in movement from normal developing children.
The Tau guide process of control can be mathematically described by the equation,
= kg
T represents the sound wave and K represents the kinematic s of a movement produced. By
applying this equation a person can vary the length (T), shape (K) and size (A) of a wave. By
measuring the size of a wave and applying the Tau guide analysis to how it varies over time,
the changes in duration can be tested to decide if they were controlled in a way that implies
Tau coupling. If the changes in duration of a wave are measured and analysed by the Tau
guide analysis, it may suggest that the changes in duration are controlled in a way that
suggests Tau coupling. The values in the equation of Pitch (T), Timbre (K) and Amplitude
(A) are all represented as different values of the sound wave. A coupling constant K value
represents the dynamics precipitousness of a movement (Lee, Schloger & Pepping,
2008.).The K value is considered the timbre of a sound. It is based on the characteristic
quality of a sound, where its origin can be understood. Higher K values represent a loud,
collision type noise to the end of a sound and suggests a long initial acceleration followed by
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a short and quick decline in sound (Lee, 1998) and lower K values represent a softer, quieter
end to a sound (Lee, Schogler, & Pepping, 2008). Within the present study, a significant
difference in k values for each twin is expected to be found. Amplitude is represented by the
A values. It suggests the greatness of extent to a sound, the scope and measure of a sound.
Pitch is represented as the T values. It determines the fundamental frequency of a sound
wave.
Autism Movement Deficits
Autistic children do not commonly have major motor disturbances. There have been
many studies that have reported minor motor deficits which manifest themselves as motor
delay and difficulties in development and coordination. There are often also abnormal
displays of movement, for example, repetitive movements (Ming et al, 2007). Leary and Hill
(1996) claimed that such motor deficits impair the communicative and social development of
a child.
Motor difficulties in autistic children have been categorised as associated symptoms
(Ming, Brimacombe & Wagner, 2007). Common ‘symptoms’ are motor delay, hypotonia,
motor apraxia toe-walking and reduced ankle mobility. A study with a cohort of 154 autistic
children found that Hypotonia was the most common problem found in 51% of the children
(Ming et al, 2007). In the same study motor apraxia was found in 34% of the children and
toe-walking in 19%. Gross motor delay was reported in 9% of the children.
It is not
uncommon for people with autism to also have dyspraxia which manifests itself as a
difficulty in controlling movement (Siegal & Blades, 2003). Whether oral or manual
dyspraxia or both, it is often easier to teach autistic individuals to use syllables of vocabulary
and pictures than speech. Language can develop a lot faster when using symbols over speech.
Notordaeme et al. (2007) conducted study that found speech deficits in many autistic
children. They concluded that not only motor deficits but also speech deficits are concurrent
with autism. Communication manifests itself in deficits of language. Every autistic will have
different deficits and their severity in each of these will vary. One quarter of sufferers will
have normal grammatical and vocal skills while another quarter may never be vocal at all
(Kjelgaard & Tager-Flusberg, 2001; Lord & Paul, 1997). Generally though, organisation of
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language, word meaning and syllable production can all be affected. Speech and recognition
and understanding of language can also be affected. It has been suggested that the level of
language ability is relative to the severity of autism (Happe, 1995). Tager-Flusberg (2006)
defined three subtypes of language ability of autistic children: high on verbal IQ, high on
non-verbal IQ and equal verbal and non-verbal IQ. They found that high non-verbal IQ
associated with a lack of communication and social interaction. This supports previous
findings that claim language development has a large influence in abilities of an autistic
individual (Happe, 1995).
The cause of difficulties in language development is unknown. Many researchers
believe the interplay of genes and the environment is the root of this disorder (Askhoomoff et
al., 2002). It is suggested the impairment of language is biological but it can be aggravated by
other difficulties such as hearing or visual impairments, motor difficulties or physical
disabilities (Boucher, 2003). Alternatively, many theorists attribute the communication
problems in autism are due to an impairment of their ‘theory of mind’. Theory of mind as
mentioned previously, describes the ability to think of or consider other people thoughts or
mindset. Autistic individuals are believed to not develop theory of mind, which leads to their
social and emotional malfunctions. This could explain why autistic children find it difficult to
interpret and interact with their environment and the people in it. However, there are many
autistic children who pass the theory of mind task and who display symptoms before this task
can be administered (Siegal & Blades, 2003). Many researchers are looking for alternative
ways to define the causes of autism and they are increasingly looking to the roots of language
impairments in autistic individuals.
Development of language in autism is often uneven. For example, an autistic child
may accelerate in reading but may fail to comprehend what they have read (Gowen, Stanley
& Miall, 2008). Often, if speech is intact, it may lack context or content. An autistic
individual may repeat words or numbers or echo words or sentences inappropriately.
Comprehension is found to be more impaired than expression. Autistic individuals often
express language that may be well formed, analysed and learned often through echoic.
However, this language may be misunderstood and revealed out with the appropriate context.
Communicating with others is an apparent problem for autism disorder. The
communication deficits of the disorder are closely related to the social deficits. Autistic
9
individuals lack the understanding of conventions and rules for socialising presenting
difficulties in attending to conversations with others. Autistic individuals also lack the
linguistic knowledge to form coherent and meaningful replies in conversation. These
linguistic and non-linguistic pragmatic impairments are strong within the disorder therefore,
language as a means of communicating with others is rare for many autistic individuals.
Autistic people may recall at length conversations they have heard or about things that
interest them but they are not capable of holding interactive conversations about these.
Language is usually repetitive and only relevant to the autistic individual (Tager-Flusberg,
1996). Conversation tends to be within the individual, for personal satisfaction and with no
expectations of external feedback from others (Fine et al., 1994). Due to the lack of
understanding of others emotions and how to interact, their language can often be
inappropriate and out of context.
Semantic organisation relates to the meaning of language. Semantic impairments vary
along the spectrum but generally autistic children will not comprehend non-literal language
such as irony or sarcasm (Happe, 1995). More severe cases have difficulties in understanding
terms that change dependant on time and places. Dunn et al. (1999) explored semantic
categorisation tasks with autistic children. They found that they could not distinguish
between deviant and target, as they could not determine the external contexts or attribute the
more common semantics to suggest which were deviant. Dawson et al. (1998) believed these
difficulties in vocabulary were related to differences in the limbic system. Many autistic
children have limited word knowledge and often cannot comprehend the meaning of
connected speech (Dunn, Vaughan, Kreuzer & Kurtzberg, 1999). Words relating to their
mind or mental state such as ‘believe’ or ‘think’ are often not within their vocabulary (Happe,
1995). This lack of semantic knowledge could reflect on their lack of ability to understand
other people’s emotions and language. However, it is known that grammatical errors are
common in autism, especially in spontaneous speech (Kjelgaard & Tager-Flusberg, 2001).
Phonology is not usually badly impaired in an autistic individual and language is
generally phonologically accurate. However, if an autistic individual is non verbal it is
assumed that just as grammatical and semantic language cannot be acquired, phonological
language will not be acquired either (Boucher, 2003). Phonology in these disorders has been
measured by event related potentials. This is an electro physical response resulting from
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thoughts or perceptions. When considering the auditory processing, autistic children have
shown a shift in the first positive peak in the ERP (P1) (Molfese et al., 2006).
More recently researchers have been trying to define subtypes of autism to better
understand the underlying causes and tailor treatments to the individual sufferers (TagerFlusberg, 2006). Researchers have claimed that subtypes of autistic children who are verbal
but have impaired language have similar language phenotypes to those of children with
Specific Language Impairment (SLI). SLI is a developmental language disorder that can
affect expressive and receptive language and they display phonological and grammatical
language difficulties, all of which are often found on autistic children. MRI scans have also
suggested that within both of these disorders there are differences in brain asymmetry
compared to normal development. Language regions in the left hemisphere are enlarged
relative the right hemisphere in normal development however, the opposite is often apparent
in children with autism and SLI (Siegal & Blades, 2003).
Acoustic Analysis
Methods to better understand infants, has long been conducted by analysing their
cries. Cry abnormalities have previously been measured on a sound spectrograph which
provided a picture of the cry that displayed the acoustic and descriptive characteristics in a
visual pattern. With the age of new technology these methods have been greatly enhanced
and computer technology has allowed acoustics to be viewed and measured from various
parameters instead of just visual patterns. Acoustic analysis in infants was initially used to
determine medical problems but is now being used to gain a better understanding of issues
such as autism.
Crying is a child’s earliest form of communication and most of the initial
communication between parent and child involves crying. While cries can sound similar to an
untrained ear most parents can distinguish a painful cry from a hunger cry. Communication
through crying is an acoustic phenomenon (Corwin, Lester & Golub, 1996.). The onset of a
cry, pitch, dynamic and patterns of a cry can determine the message of a cry. Cry analysis is
an acoustic analysis of the sub-second psychoacoustic parameters in it, such as timbre, pitch,
intensity. Crying analysis has been useful in researching many medical and social difficulties.
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As a result there have been many medical and social problems that have been found to affect
the cry production system such as brain damage (Corwin, Lester & Golub, 1996). They found
that brain damaged infants cries were higher pitched, more dynamics in terms of the rising
and falling of waves, and a more unstable fundamental frequency (Ostwald, Peltzman,
Greenberg, & Meyer, 1970). It is hoped that autism supports these findings and affects the
cry production system so that it can be traced at a much earlier prognosis stage.
The communication between a parent and child is not just thought to be one sided but
is more representative of a conversation between two people despite neither side
understanding the language being spoken. The communication must be deeper than just
lexical meaning and syntax (Malloch, 1999). Both parties appear ‘attuned’ to the physical and
vocal gestures of each other and the communication bears resemblance to a rhythm or a
melody, representative of music. This attunement between the two is thought to be an
important aspect of the parent and child’s bond and without it can lead to many problems for
either party such as depression in the parent (Beebe & Lachmann, 1994) and developmental
delay in the child (Hauge & Hallan Tensberg, 1996). ) This co-operative rhythmical
communication between a parent and child has been termed ‘communicative musicality’
(Malloch, 1999). Communicative musicality describes mother-infant proto-conversations.
Malloch (1999) describes this as the pulse, quality and narrative in a musical dialogue. He
considers these elements to be the aspects of human communication that are represented in
music and allow a conversation to occur. Pulse is “the regular succession of expressive events
through time” (Malloch, 1999). Such events could be a change in the pitch, the start or end of
a vocalisation or a louder or quieter moment. Quality is the second component of
communicative musicality. It represents the timbre and melody of the vocalisations. If we
were relating the vocalisations to physical movement, the quality would represent the speed
and contour of the body movements. The final component relates to the narrative. The
narrative is based on the pulse and quality of the vocalisations. Narratives represent the act of
sharing a communicative experience with another person. Combining these three components
communicative musicality represents the act of emotional transference and companionship
between two people. Each of these three areas represents a movement that occurs between a
parent and child to allow an understanding with one another. Pulse, quality and narrative are
the three main factors that represent this musical element to human communication. This
rhythm and attunement attained in the communication between parent and child may not
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always function or be present which can explain some development delays such as autism
(Hauge & Hallan Tensberg, 1996).
Just as people are, many infants are better at communicating than others. With the
difficulties in communication that autistic children experience it could be suggested that their
cries are more difficult to interpret. This suggestion depends on a couple of things. Whether
the later language problems experienced by autistic children stem from early communication
difficulties in their cries and also whether the onset of autism can even be determined and
examined that early.
This study aims to examine the cries of a young infant who is later diagnosed with
autism. Her cries will be compared with those of her identical twin sister who does not later
get diagnosed with autism. The pitch, intensity, length, dynamic and frequency of their cries
will be examined to determine any differences using a new acoustic analysis technique based
on prospective control, as described above. The methodology is based on the principles of
General Tau Theory and works to measure the degree of control the children have of the pitch
(T), amplitude (A) and timbre (K) of their vocalisations.
This study focuses on early development communication to help determine autism at
an earlier stage. Trevarthen and Daniel (2005) have used home video tapes to analyse
dynamic and timing elements of monozygotic twins, one of which later developed autism
while the other developed normally. Their research found differences in the children’s
movement and interactions. In particular, the twin developing autism exhibited a lack of
purposeful movement suggesting a core deficit in prospective control. The current study will
use the same infant data, extracting the acoustics of vocalisations made by the twins to test
for differences in their prospective control of the voice. General Tau Theory will be
employed in this analysis and tau coupling will be used to determine whether there is a
difference in percentage between the twins. This study will analyse the acoustics of each
twin’s vocalisations to measure their intentional, prospective control of pitch, timbre, and
intensity using a novel methodology based on general, amodal principles of prospective
control (General Tau Theory; Lee et al., 1999).
It is hoped there will be a significant difference in the amount of intentional control
each twin has on their vocalisations. The twin who later developed autism should exhibit less
13
acoustic control if these findings are to support previous research (Trevarthen and Daniel,
2005; Malloch, 1999). It is suspected that the child who is later diagnosed with autism will
present lower tau coupling percentages and different values regarding the control of pitch,
timbre and amplitude than the normally developing twin. This study aims to establish the
acoustic analysis programme as a reliable and valid means for determining vocal actions
which can be widely used among other studies. This study aims to aid further the research
into early diagnosis of autism by establishing a new approach of how to analysis children at
an earlier age. It is hoped this study will further establish home videos a useful tool in
analysing the early signs of children with autism.
Hypothesis
There are differences in a neural, amodal prospective control system that are
detectable shortly after birth in the vocal ‘movements’, the acoustic change of the baby’s cry.
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Methods and Materials
Design
This study analysed the acoustics of eight infant cries from two monozygotic twins
taken from home video tapes three days after birth, to determine if there were differences in
the intentional control of the infants’ vocalisations. The control of their pitch (T), timbre (K),
and intensity (A) were measured to determine the degree of control of the voice. This analysis
of the voice was carried out using proprietary software designed for this purpose. Baby B
refers to the child who went onto develop autism. Baby J refers to the baby who went on to
have a normal development. They are labelled according due to the first letter of each of their
names.
Participants
The study involved two monozygotic twins. The footage used examined their vocal
actions from birth to 11 months of age. One of the twins was diagnosed with autism at the
age of two using the ICD-10, while the other twin had a normal development.
Materials
The materials used in this study included video footage of the twins from birth to 11
months of age given by the consent of the parents, the acoustic analysis programme, and a
computer. The acoustic analysis required several types of software available within the
computer. To listen to and edit out the clips of sound, pro cut or QuickTime can be used.
Microsoft excel was used to achieve spreadsheets of the data. A programme called Praat
(available to download online) was used to analyse the wave processing. The analysis TauG
programme was based at the Perception in Action Laboratory at the University of Edinburgh.
Procedure
Two home video tapes of the twins ranging in age from birth to 11 months old were
examined for any vocal utterances. Vocalisations, providing they were of audible quality,
were selected and analysed to determine the amount of control they had on their intentional
vocalisations. This was analysed using an acoustic analysis programme based on General Tau
Theory developed by the Perception and Action Laboratory at the University of Edinburgh.
Intentional control was measured through the pitch, timbre and intensity of their
vocalisations.
15
Statistical Analysis
This acoustic analysis draws on the basic principles from General Tau Theory (Lee et
al., 1999). This form of analyse has previously been used to study Parkinson’ disease
(Freeman et al., 1993.) and neonatal babies (Craig and Lee, 1999; see Lee 2005). General Tau
Theory is a neurological mechanism that our bodies use to achieve movement. It represents a
means for researchers to examine how different aspects of a sound (duration, size and shape)
may be controlled for through communication. This theory claims that in order to produce
prospectively controlled behaviour of vocalisations; we perceive the amount of change in a
sound wave relative to the variable being measured. This process of control is described
mathematically by General Tau Theory and is now integrated into a working programme
which analyses the data through a computer. General Tau Theory measures the motion gaps
of a movement. A successful vocal movement requires the motion gaps to close at the same
time, this is called Tau Coupling. The Tau coupling percentages will be analysed. Higher
percentages (>90%) representing more successful movements. There will further be two
levels of analysis. The level of analysis measures each wave so that each waveform has three
variable; T, K, and A. T represents the ‘pitch’ or fundamental frequency (F0). K is the
coupling constant from the Tau Theory and represents the ‘timbre’. A represents the
amplitude of the wave and is considered to reflect the intensity of a wave. The second stage
of analysis presents T, K, and A wave glides. The T, K, and A values are expected to fit with
the prospective control of movement. This analysis aims to determine the amount of
perspective control each twin has in their vocalisations.
16
Sound wave
1ST Tier analysis
2nd Tier analysis
%
r2
Pitch glides
T
K
%
T
A
r2
A
T=
Duration
%
K=
r2
Timbre
T
Timbre
glides
K
A
A=
Amplitude
%
r2
T
A
K
Amplitude
glides
Figure 1. Schematic of the accoustic analysis performed in this study. The waveform was measured for basic parameters
T, K, and A, which are measures of the fundamental frequencey (F 0), timbre, and intensity of the sound wave. Each of
these measures was achieved using a calculation of the ‘up’ and ‘down’ movement of the pressure ramps in the sound
data. T is a measure of the duration of a complete up and down cycle, and thus represents the primary, or fundamental
frequency. A is a measure of the height of the wave, and so gives an approximate indication of the psychoaccoustic
intensity of the sound. K is a value derived from a tauG analysis of the ‘up’ and ‘down’ portions of the wave. It loosely
represents the compiled harmonics (Fn+1) in a single value and is specifically the coupling constant of a tauG measure of
each ‘up’ and ‘down’ portion of the wave. These data produce the ‘1 st Tier Analysis’. The ‘2nd Tier Analysis’ measures
the control of the shifting T, K, and A values over the course of the cry using a tauG analysis. The values %, r2, T, K, and
A each give information about the form of the control of the changes in the pitch, intensity, and timbre over the course of
the cry.
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Procedure of Statistical Analysis
(See appendix 1 for more detailed description of the protocol for the analysis stages).
Video edit and audio transfer
Sound clips of either twin’s cries were edited out of the video using Final Cut Pro.
Times and occurrences in each clip were noted also. The clips were then edited as sound
wave audio files. The audio files were each opened using Praat and converted into text files.
First tier of analysis
Matlab was opened and within it the analysis program, called TauGUI was opened. In
TauGUI the newly converted text files of the sounds were opened. Criteria were placed into
the program as follows: Sample rate; 44000Hz, Smoothing filter- 4. Once this was completed
the ‘Run section seeker’ button was selected until the program informs you that it is
completed. The following criteria were then inputed: max duration- 0.5, min duration0.00005, max amplitude- 1.5, and min amplitude- 0.001. The ‘Run test’ was selected until the
program once again informs you that it has completed that part of the analysis. All of the data
was selected and analysed and the results were exported as text files. These stages were
completed for every sound clip.
To determine the percentage tau coupling criteria the data was pasted into
Kaliedograph and smoothed. To determine the T, K, and A values the data was opened in
Microsoft excel and then into Kaliedograph, again to be smoothed with a sigma of 4. This
presented us with the T, K, and A values and the tau coupling % for the first stage of the
analysis.
Second tier of analysis
In matlab, the workspace was cleared and the data was imported. The ‘Resample’ file
was opened in the current directory. The start and end time of the sound clip was divided by
0.002 gives the number of steps. The number of steps was added into line 8 of the file and the
‘save and run’ button was pressed. In the workspace new files appeared and these were
exported into text files and saved. Each individual file represented the T, K, or A glides.
The TauGUI file was opened again in Matlab and the T, K, and A files were each put
through the analysis again. A sample rate of 500Hz was selected along with a sigma value of
4 and a 5% velocity. Again, the files were exported and saved as text files. This presented us
with the T, K, and A glides and the % tau couplings for the second part of the analysis.
18
Results
Both twins displayed high percentage tau couplings. Both of the babies mean
percentage tau couplings were 86.60%. However, neither presented significant percentage
couplings (>90%). This suggests a great deal of control is equally present in both babies.
For K values baby B had a lower tau coupling % than baby J although neither
percentages were significant (>90%). Baby b had much higher values for the K value
overall. Baby B had significantly higher amplitude and timbre than baby J which suggested
that baby B’s vocalisations were much more intense and dynamic in the timbre than baby J.
Baby B also had much higher K values for pitch but not a significant amount more.
Baby B showed a higher mean amplitude % tau coupling than baby J. The A values
for timbre, amplitude and pitch were all higher for Baby J. However, only the A values for
timbre were significant which suggests that there is more dynamic in the amplitude of baby
J’s vocalisations. The amplitude and pitch were higher but not significantly so.
Baby J had a higher T value tau coupling percentage than baby B although it was not
significantly high (>90%). Baby B had higher T values for timbre, amplitude and pitch in
comparison to baby J. Baby b had significantly higher amplitude and dynamics regarding
pitch. Baby B also had higher frequency for the pitch but it was not significant.
There were two parts to analysis and the results are presented accordingly. The first
tier of the analysis examines Tau coupling percentages for T, K and A waves. The second tier
of analysis examines the glides of the waves with regards to the duration (T), timbre (K) and
amplitude (A) of each wave (see the diagram of statistical analysis in the methodology).
Wave Percentage Couplings (%)
The wave percentage represents the amount of Tau coupling present in the sound. If
the Tau coupling percentage is higher than 90% then this suggests good control of
vocalisations. If the Tau coupling percentage is below 90% then internal or external causes
may have affected the amount of control the child has on their vocalisations (e.g. neuromotor,
intrinsic Tau guide). Baby B, who later develops autism, is expected to have lower
19
percentages. This is because autistic children are expected to have less control and perhaps be
more affected by other causes.
The duration of each clip varied slightly. The longest clip lasted 0.043 seconds for
baby B and the shortest clip lasted 0.011 seconds for baby J (see Table 1). Baby B had a
longer mean length of clips (0.023 seconds) than baby J did (0.012 seconds).
Table 1: Duration of each clip
Duration
BabyB Clip 1
0.018
BabyB Clip 2
0.043
BabyB Clip 3
0.018
BabyB Clip 4
0.015
Baby J Clip 1
0.013
Baby J Clip 2
0.012
Baby J Clip 3
0.011
Baby J Clip 4
0.013
Table 1. The duration of each clip in seconds. There were eight clips in total, with four for
each baby. The clip duration was the whole length of the baby’s utterance. The utterances
were not clipped in length.
Table 2: Tau coupling percentages means (%) of both twins’ wave forms
Baby B
Baby J
Clip 1
88.70
86.59
Clip 2
86.33
85.80
Clip 3
87.00
87.07
Clip 4
85.20
88.11
20
Table 2. Represents the Tau coupling percentages means of both twins’ wave forms. The
separate percentages are presented for each of the four clips for each baby.
Mean percentages of Tau couplings for both babies were compared. There was not a
large amount of variability between the clips regarding percentage coupling for baby B. Clip
4 had the highest percentage coupling of 88.11% yet it still did not reach the significant level
of 90%.
There is a similar degree of variability in baby J compared with baby B (see Figure 1).
Clip 1 had the highest percentage coupling for baby J (88.70%) but again it was still not high
enough to reach the 90% significance level. Neither of the babies had a great range of
percentage couplings. Both babies’ had similar and consistent percentages which suggest
little difference between the controls of their vocalisations.
Both twins presented high percentage couplings (%) but neither reached the
significant level of 90%.The overall percentage coupling for each baby was the same
(86.60%). This suggests that there was little difference overall in percentage coupling
between the vocalisations of the twins. However, there was a slight difference in standard
deviation. Baby B had a standard deviation of 9.5 and baby J had a standard deviation of 11.
This indicates baby J has a greater variability in the percentage coupling. Although neither
reached the significant percentage level, an average of 86.60% was still very close. This
could suggest a great deal of control is presented in both babies regarding their vocalisations.
K value (Timbre)
1st Tier analysis
The mean percentages for timbre values were examined in the 1st tier of analysis. It
was found that baby J had a much more consistent % of waves for K over the four sound
clips (see Figure 2). In comparison baby B’s % was much more diverse in wave % of K over
the four clips. Baby B had much more variance in the % (4.89) than baby J (0.84) and a much
higher standard deviation (2.21) than baby J did (0.91).
21
Baby B (who later developed autism) had a lower k value percentage overall
(85.52%) than baby J (86.31%), but this was not significant (<90%). However, baby B had a
higher standard deviation (17.45) than baby J (16.32).
Figure 2. Presents the wave percentage of timbre. The timbre relates to the dynamics of a
movement. The percentage amount represented the closure gaps of the timbre of their vocal
movements. There are four clips displayed for each baby with a mean percentage each.
Higher percentages (>90%) represent a good prospective control of the timbre of their vocal
movements.
2nd Tier analysis
In three out of the four clips baby B had higher K glides than baby J (see Figure 3).
This means in these three clips they had a much more intense quality of sound. The mean K
glide for amplitude in baby B was higher (0.035) than for baby J (0.028). The standard
deviation for both baby B (0.01) and baby J (0.01) were very similar. A t-test was conducted
that determined the difference in K glides for amplitude between the two twins was
significant (t= 0.075, df=6, p<0.05). This confirms that the baby B displayed much more
dynamic, intense quality of sound and ended with louder, collision sounds at the end of
vocalisations. A further ANOVA was conducted on the data which confirmed the
significance difference of baby B’s much louder and dynamic vocal sounds in comparison to
baby J’s quieter vocal sounds (F=0.56, df= 1,6, p=<0.05).
22
Figure 3. Displays the K glides for amplitudes. The numbers represent the amount of K value
of amplitude in each of the four clips for each baby. The K glides reflect the timbre or
dynamics of a vocal movement. Amplitude represents the intensity of a vocal movement.
From the K glides of the timbre it is clear that the dynamics of baby B’s vocalisations
are much higher in every clip than for baby J (see Figure 4). This data again confirms that
baby B presents much louder and dynamic ends to their sounds suggesting this twin has less
control of her vocalisations. The mean K glide for baby B was higher (0.40) than baby J
(0.37) as well as the standard deviation was much higher for baby B (0.02) than for baby J
(0.01). A t-test was conducted which confirmed that baby B’s vocalisations were significantly
louder and more dynamic than baby J’s (t= 2.65, df= 6, p= <0.05).An ANOVA was
conducted which also confirmed that baby B’s vocal sounds were significantly more dynamic
than baby J’s (F=7.04, df= 1,6, p= <0.05).
23
Figure 4. Displays the K glides of the timbre. The K values that are presented represent the
amount of K that is in the timbre of the vocal movements of each of the four clips displayed
for each of the babies. K represents the timbre or dynamics of a vocal movement.
From the K glides of the pitch it is clear that baby B has higher K glides. In every clip
baby B has a higher k glides for timbre than baby J (see Figure 4). Baby B has a higher mean
(0.02) than baby J (0.01). The standard deviation is also higher for baby B (0.01) than for
baby J (0.00). A t-test was conducted which suggested the difference in pitch for K glides in
baby B was not significantly different to baby J (t=1.71, df=6, p= >0.05). An ANOVA was
conducted to confirm that there was no significant difference between the twins in their pitch
for K glides (F=0.077, df=1,6, p>0.05).
Figure 5. Presents the K glides of pitch. The amounts presented represent the K glides of
pitch found in each of the four clips for each of the babies. K values represent the timbre or
dynamics of a vocal movement. Pitch represents the fundamental frequency of a vocal
movement.
A value (Amplitude/ Intensity)
This value considers the loudness and intensity parameters controlling. This value
represents the greatness of extent of the vocalisation.
24
1st Tier analysis
Baby B (who went on to develop autism) showed a higher amplitude percentage
coupling overall (86.44%). Baby J (who had developed typically) showed a slightly lower
percentage coupling overall (84.77%) (see Figure 6). The standard deviation was higher for
baby J (19.21) than for baby B (17.20). Baby J had much more variance in the % (7.87) than
baby B (6.22) and a higher standard deviation (2.81) than baby B did (2.49).
Figure 6. Displays the wave percentage of amplitude. Each value presented represents the
amount of tau coupling percentage present in each of the four clips for each of the babies.
Amplitude reflects the amount of intensity in a vocal movement. Higher percentages (>90%)
represent a good prospective control of the intensity in their vocal movements
2nd Tier analysis
The A glides for amplitude were analysed and it was suggested that the mean A glide
for amplitude in baby B was lower (0.022) than for baby J (0.023). The standard deviation for
baby B was much higher (0.007) than for baby J (0.005) (see Figure 7). A t-test was
conducted that determined the difference in A glides for amplitude between the two twins
was not significant (t= 0.10, df=6, p>0.05). A further ANOVA was conducted on the data
which confirmed no significance difference in amplitude between the twins (F=0.011, df=
1,6, p=>0.05).
25
Figure 7. Represents the A glides for amplitude. The numbers displayed represent the
amount of A glides for amplitude found in each of the four clips for each baby. A glides
reflect the amount of intensity in a vocal sound.
While analysing the A glides for timbre it was clear that in every clip baby J has a
higher amplitude quality than baby B (see figure 8). The mean A glide for timbre is higher for
baby J (0.42) than for baby B (0.36). The standard deviation was also higher for baby J (0.07)
than for baby B (0.03). A t-test was conducted that determined the difference in A glides for
Timbre between the twins was a significant one (t=0.17, df= 6, p=<0.05).An ANOVA was
also conducted which confirmed the significance of the difference in A glides for Timbre
between the twins (F=2.5, df=1,6, p=<0.05).
Figure 8. Displays the A glides in timbre. The A values presented reflect the amount of A
glides of timbre in each of the four clips for each baby. A glides represent the amount of
26
amplitude or intensity in vocal movements. Timbre represents the dynamics of vocal
movement.
From analysing the A glides of pitch it was found that in three out of the four clips,
baby J had higher A glides for pitch (see Figure 9). The mean A glides for pitch was higher
for baby J (0.02) than for baby B (0.01). The standard deviation was also higher for baby J
(0.005) than for baby B (0.002).A t-test was conducted which confirmed that the difference in
A glides for pitch between the twins was not a significant one (t=1.29, df=6, p= >0.05).An
ANOVA was conducted which confirmed that there was no significant difference between
the twins for A glides for pitch (F=4.82, df= 1,6, p= >0.05).
Figure 9. Displays the A glides of pitch. This figure displays the amount of A values of pitch
presented in each of the four clips for each baby. The A glides represent the amplitude or
intensity of a vocal movement. The pitch (T) represents the amount of fundamental frequency
within the vocal movement.
T value (Frequency/Pitch)
1st Tier analysis
While analysing the wave % of pitch it was found that baby B (who later developed
autism) had a lower T value percentage overall (84.49%). Baby J (who developed typically)
had a slightly higher percentage overall (85.33%). The standard deviation is higher for baby J
(2.97) than for baby B (1.42).
27
Figure 10. Presents the wave percentage of Tau couplings for Pitch (T). The numbers given
represent the amount of percentage given to each of the four clips for each of the babies. The
higher the percentage (>90%) the more controlled the vocal movement is considered to be.
Higher percentages (>90%) represent successful tau coupling and therefore, more successful
movements. Pitch (T) represents the fundamental frequency of a vocal movement.
2nd Tier analysis
In three of the four clips baby B had higher T glides for Amplitude. The mean T
glides of amplitude is higher for baby B (201) than for baby J (180) (see Figure 11). The
standard deviation however, is much higher for baby J (103.07) than for baby B (12.09). A ttest was conducted which concluded that the difference in T glide of amplitude between the
twins was a significant one (t=0.42, df= 6, p= <0.05). An ANOVA was conducted which
confirmed that Baby B had significantly higher T glides for amplitude than baby J did
(F=0.176, df= 1,6, p= <0.05).
28
Figure 11. Presents the T glides of amplitude. The numbers presented represent the amount
of T values of amplitude in each of the four clips for each of the babies. T glides reflect the
fundamental frequency of a vocal movement. Amplitude represents the intensity of a vocal
movement.
While analysing the T glides for timbre it is clear that in three of the four clips baby B
had slightly higher T glides for Timbre (see Figure 12). The mean T glide for timbre was
slightly higher for baby J (0.42) than for baby B (0.40). The standard deviation however, was
higher for baby B (0.04) than for baby J (0.03). A t-test was conducted which concluded that
there was no significant difference in T glides for timbre between the twins (t= 0.57, df= 6,
p= >0.05).An ANOVA was conducted which confirmed there was no significance difference
between the twins for T glides of Timbre (F=0.32, df= 1,6, p= >0.05).
29
Figure 12. Displays the T values of timbre. The numbers presented in figure 12 represent the
amount of T values of timbre in each of the four clips in each of the babies. The t values
reflect the fundamental frequency of a vocal movement. The timbre (K) represents the
dynamics of vocal movements.
When considering T glides for pitch, all four clips baby B had much higher T glides
for pitch (see figure 13). Baby B had a higher mean of T glides for pitch (0.02) than baby J
did (0.01). Baby B also had a much higher standard deviation (0.01) than baby J (0.0). A ttest was conducted which confirmed the difference in T glides between the twins was a
significant one (t=1.72, df= 6, p= <0.05). An ANOVA was also conducted which confirmed
that baby B did have significantly higher T glides for pitch than baby J had (F= 2.94, df= 1,6,
p= <0.05).
Figure 13. Displays T glides of pitch. The figure above represents the amount of T values of
pitch present in each of the four clips for each of the babies. The T values represent the pitch
or fundamental frequency of a vocal movement.
30
Discussion
Wave percentage Tau couplings (%)
The wave percentages of Tau couplings for each twin were looked at, taking into
consideration all four clips from each twin to make an average percentage. The percentages
of tau coupling were examined to determine the control of each wave to see if that could
highlight how successful their vocal movements were for each twin. It was suggested that had
there been a difference in the percentage couplings of the twins, there would have been
differences in how each baby managed the closure of motion gaps. If the % coupling is
higher than 90% then good control of vocalisations is displayed. If below 90% then internal
or external causes affect the amount of control they have. (E.g. neuromotor, intrinsic tau
guide). Baby B (the autistic twin) was expected to have lower percentages as they are
expected to have less prospective control and perhaps be more affected by other causes. This
study of course is particularly speculative and serves as a foundation for further research into
this area. It was thus predicted that baby B who later went on the develop autism would have
a significantly lower percentage for the tau coupling than the normally developing child, baby
J. This would support the hypothesis that autistic children lack the internal tau guide to aid
their movements which would explain their movement difficulties. However, such
differences in percentages were not evident between the twins.
Concerning the data showing the control of the gap closures of each wave, there was
not a great deal of variability between the clips regarding percentage coupling for baby B.
Clip 4 had the highest percentage coupling of 88% yet it still did not reach the significant
level of 90%. There is a similar degree of variability in baby J compared with baby B. The
highest percentage coupling for baby J was 89%, but again it was still not high enough to
reach the 90% significance level. Neither of the babies showed a great range of percentage
couplings in their sound wave motion-gap closures. Both babies had the same mean tau
percentage coupling of 89%. These same and consistent percentages suggest little difference
between the twin’s vocalisations, but it is interesting to note that this may refer to a specific
newborn form of acoustic control that can be explored further in subsequent research.
In general, percentages were high for both babies however; no clip’s percentage
reached the 90% significant level. This suggests that neither baby had great prospective
control of their vocalisations. However, there was a slight difference in standard deviation
31
which indicates baby J has a greater variability in the percentage coupling. The mean
percentage couplings for each twin are the same which indicates there is no difference in the
control of closing the motion gaps. This indicates no differences in prospective control of
vocal movements between the twins.
These results suggested that there is very little difference between the twins control of
their sound waves indicating that baby B’s autistic development has not affected this area of
vocalisation. The specific variables of timbre, amplitude and pitch are now considered to
determine if there is a difference within them between the twins. This would highlight more
specific differences between the twins that will further research more focused.
K value (Timbre)
The K value depicts the amount of Tau coupling percentage for timbre and the K
glides within the sound waves. The K value represents the dynamics of a movement. This
value looks at the timbre changes parameters controlling. High K represents a sound that ends
with a loud, collision type end to a sound and a low K represents a much quieter end to a
sound. Baby B (who developed autism) had a lower Kvalue percentage overall (86%) (See
Table 3). Baby J (who developed typically) had a slightly higher percentage coupling. This
supports the idea that baby B would have lower tau coupling percentages which represent less
prospective control of their vocalisations.
A significant difference in K values for the amplitude of the timbre (K) glides
between each twin was expected to be found. The results found that there were differences in
K values between the two babies. Baby B was found to have significantly higher K values
regarding the amplitude of the K glides. (F=0.56, df= 1,6, p=<0.05).This suggests that baby
B’s vocalisations had a much more intense quality of sound and tended to end with much
more dynamic and louder sounds. A further ANOVA was conducted on the data which
confirmed the significance difference of baby B’s much louder and dynamic vocal sounds in
comparison to baby J’s quieter vocal sounds. It could be suggested that the increased
intensity and varying dynamic of baby B’s vocalisations present a less controlled
vocalisation. A prospectively controlled vocalisation may present itself as less stressed and
rounded at the ends of the sound waves and the quality of sound may not be as intense. The K
values for timbre glides also presented differences between the twins (F=7.04, df= 1,6, p=
<0.05). Again, baby B’s vocalisation were much louder, they were more dynamic and ended
32
with more collision force. Comparing these findings with the wave percentages it is clear that
the higher K values represent less quality in the tone of the voice and therefore less
prospective control of their vocalisations. These findings suggest again that baby B has less
prospective control over vocalisations. The K values from pitch also suggested differences
between the twins. Baby B presented higher K values for pitch than baby J. However, unlike
the other differences this one was not significant. (F=0.077, df=1,6, p>0.05).
Beginning and ending with more forceful, collision style sounds is representative of
the irregularities within autistic language abilities (Dunn, Vaughan, Kreuzer & Kurtzberg,
1999). This was supported by the general K value results. It was expected that the data would
indicate different K values between the twins and the results supported this hypothesis. The
term ‘pulse’ described by Malloch (1999) represents events within a sound, such as louder or
quieter beginnings or endings to a sound. Within the results it was found that baby B
displayed much higher values of K glides which suggest a more forceful sounding
vocalisation. This relates to the Malloch’s term ‘pulse’ within communicative musicality
since, baby B has more dynamics at the beginning and end of their vocalisations (1999). This
suggests that baby B has a different pulse form baby J. This may suggest a difference in
rhythm of communication between baby B communicating with their parents and baby J
communicating with their parents. Malloch’s term ‘quality’ refers to the timbre of a sound
(1999). Given the current results it is clear that baby B and baby J have different qualities to
their sounds. Further research could be done to consider the K values here and specifically
analysis whether the different rhythmical styles of the twins to their parents suggest that one
style is less effective. If this is the case, then the ineffective communication between parent
and child could be an early indicator for further communicative difficulties including autism.
A value (Amplitude/ Intensity)
The A value represents the loudness and intensity parameters controlling. The Tau
coupling percentages (%) for the amplitude glides (A) were examined. This value represents
the greatness of extent of the vocalisation. Baby B (who went on to develop autism) showed a
higher amplitude Tau coupling percentage overall (86%). Baby J (who had developed
typically) showed a slightly lower Tau coupling percentage overall (84%). This suggests that
baby B had a more intense sound and more prospective control over their vocalisation than
baby J. Although neither of the values were significant at the 90% level. However, the
33
specific amplitude glides were not more significantly intense for amplitude for baby B (t=
0.10, df=6, p>0.05).
The A glides for timbre were much higher for baby J than baby B. This suggests that
baby J displayed a significantly higher quality of intense vocalisations than baby B (F=2.5,
df=1,6, p=<0.05). This would support the hypothesis that baby J’s vocalisations have a better
quality and this may be because she developed normally in comparison to baby B. Malloch’s
(1999) term, ‘quality’, would suggest that baby J had better contours of movement within the
sound waves of her vocalisations. The quality of a sound is crucial for the interpretation of
its meaning therefore; the lower quality of amplitude that is presented by Baby B may
suggest a difficulty or at least a difference in the way baby B communicates. Due to the lack
in quality of amplitude, crying may be harder to interpret which can be a crucial part of the
communicating (Corwin, Lester & Golub, 1996). Baby J also had higher A values for pitch.
The mean A values for pitch was higher for baby J (0.022) than for baby B (0.019). However,
the difference was not confirmed as a significant one (t=1.29, df=6, p= >0.05).. The lower
quality of amplitude may mean they have difficulty in communicating to their parents; they
may not be fully attuned to each other and therefore, not reach communicative musicality
(Malloch, 1999).
T value (Frequency/Pitch)
This value considers the pitch and frequency changes parameters controlling. Pitch
explains the way in which a vocalisation is controlled. Regarding the Tau coupling
percentages for the pitch glides (T), baby B (who later developed autism) had a lower T value
Tau coupling percentage overall (84%). Baby J (who developed typically) had a slightly
higher Tau coupling percentage overall (85%). This suggests that baby J’s pitch was much
higher than baby B’s. This higher percentage represents more simultaneous closure of motion
gaps and therefore, more successful vocal movements. These results support the hypothesis
that baby J has more prospective control of her vocal movements than baby B. However;
neither of the babies Tau coupling percentages for T values reached the significant level of
90%.
While considering the T values for amplitude it was clear that the mean T value of
amplitude is higher for baby B (201) than for baby J (180). This was proven a significant
difference between the twins (F=0.176, df= 1,6, p= <0.05). This indicates that the intensity of
34
the pitch was significantly higher for baby B. Pitch represents the fundamental frequency of a
sound. Regarding the T values for timbre it is clear that baby B (0.40) had slightly higher
mean T values for timbre than baby J (0.37). This suggests that baby B presents a more
dynamic vocal pitch within their sounds. However, this difference was not a significant one
(t= 0.57, df= 6, p= >0.05).
Baby B had a higher mean of T values for pitch than baby J did (0.012). This
difference was proven significant (F= 2.94, df= 1,6, p= <0.05). This again suggests that baby
B controlled their vocalisations differently from baby J. This difference will relate to the
differences in timbre, amplitude and pitch presented in this study. It also confirms that baby
B has more pitch changes and frequency within her sound glides.
From the results presented it is clear that the twins do have a different prospective
control of movement. Baby B has significantly higher K glides for timbre and amplitude as
well as significantly higher T glides for timbre and amplitude. Baby J has significantly higher
A glides for amplitude. In general, both twins presented the same Tau coupling % of 86%.
However, in specific areas the babies presented different strengths. Baby J had higher tau
percentage couplings for timbre and pitch glides however, baby B had higher Tau coupling
percentage for amplitude glides. These results suggest that both twins control their vocal
movements differently and present different vocalisations. The question remains regarding
what accounts for this difference. The tau coupling percentages represent the amount of
simultaneous closure within the motion gaps. A higher Tau coupling percentage represents a
more successful movement with more prospective control. The babies clearly vary in
prospective control depending on the variable. A prospective of control is very important in
perceiving the world around us and gives us the ability to act ahead of time. Baby J has
broader ability of prospective control given that she has higher tau coupling percentages for
both timbre and pitch. This may suggest that baby B has a weaker prospective control of her
movements. This would support findings that autistic children, such as baby B, have less
control of their movements, especially when they were self-generated (Glazebrook, Elliot &
Szatmari, 2008). It was also found that autistic children struggle to understand their own
motor intentions which may explain the lack of ability to control those actions (Pierno et al.
1996). Lee et al. (1999) previously found that people who suffer from Parkinson’s disease
also have difficulty with self-generated movement. This problem was traced to a lack of
intrinsic Tau guide (Lee et al. 1999). Their movements were a problem as they had nothing to
35
couple their movements to. This question whether there is a core deficit in such a system as
the intrinsic Tau guide (Lee, 1998) or the intrinsic motive pulse (Trevarthen, 1999). It
certainly appears that there is something missing in these diseases regarding movement and
this explanation currently seems a valid one. Given that the twins vary on the glides that they
have significant differences in, it would be unfair to assume that either has no intrinsic Tau
guide at all. Instead it could be said that Tau guides are present but more available to some
people than others. Further research would be useful to determine which neural system is
affected by these difficulties. The basal ganglia produce the kind of consistent, patterned
firing that would be required for an intrinsic timing system and is also disrupted in
Parkinson’s disease (Lee et al., 1999). This may also explain the differences between the
twins. Further research could consider movement and prospective control as the same or at
least part of the same perceptual-motor system.
This study stands in contrast to the claims made by Askhoomoff et al. (2002). They
claim that the interplay of genes and environment is the root of the autistic disorder. It is
suggested the impairment of language is biological but it can be aggravated by other
difficulties such as hearing or visual impairments, motor difficulties or physical disabilities
(Boucher, 2003). In the case presented here, there were no physical differences in the twins
known at the time of writing, after the first twelve years life. We can conclude that the
differences found here in their vocalisations were not due to physical causes, since both of the
subjects were monozygotic twins there are clearly no biological differences between the two.
The differences found in the study between the twins’ vocalisations are clearly not in any
way physically or biologically related in this case. Although this cannot be generalised to all
cases of autism, it does indicate that genetic or physical attributes are not the whole reasoning
behind language impairments or autism disorder in general. This case could support the
theory of mind hypothesis or at least lead to further investigation about the nature of the
theory (Siegal & Blades, 2003).
Pitch, as with timbre, relates to Malloch’s (1999) term of ‘pulse’. The changes in
pitch are considered differences in pulse. Again, the affect pitch changes have on the pulse
element of communicative musicality can reflect how well the child communicates to their
parent. In this case baby B had higher frequency of pitch changes and we could assume from
this that her vocalisations were less consistent and more erratic. Having so many pitch
changes and inconsistent sounds can cause problems in the rhythm of communication
36
between the parent and baby B. If pulse is affected then there is not strong attunement
between the parent and child. This supports the notion that baby B’s vocalisations were
weaker than baby J’s.
In many of the results for tau percentage couplings, timbre, amplitude and pitch
glides, the percentage do not reach the 90% significant level. However, it does reach very
close in many cases and further, more in depth analysis shows that areas of that variable are
significant. It could be considered within the analysis to lower the percentage value for
significance perhaps to 85% which would enable much more of the tau coupling to be
significant and perhaps then more accurate.
It was expected there would be a significant difference in the data to show a
difference in the amount of intentional control performed by each twin during vocalising.
The twin who later developed autism exhibited less acoustic control regarding pitch, timbre
and amplitude than the normally developing twin in all variables which supported previous
research (Trevarthen and Daniel, 2005; Malloch, 1999). It was suspected that the child who is
later diagnosed with autism would present lower tau coupling percentage. However, there
was no significant difference between the twins in terms of their general intentional control of
their vocalisations. This may be because it is still too early to detect. It may relate to the
methods used which may not be as effective as initially thought with such early language
capabilities. This study is just simply trying to establish a new way of analysis that is not
intrusive and allows us to determine whether or not there are earlier signs of autism that are
informative, worth looking at and have been missed or are difficult to find by pre-existing
techniques of analysing or autism diagnosis techniques. This study aims to establish the
acoustic analysis programme as a reliable and valid means for determining vocal actions
which can be widely used among other studies. This study aims to aid further the research
into early diagnosis of autism by establishing a new approach of how to analysis children at
an earlier age. It is also hoped this study will further establish home videos a useful tool in
analysing the early signs of children with autism.
Further research would be beneficial in this area of study. Perhaps comparing the
timbre, amplitude and pitch findings with the different sub types of autism current being
researched (Tager-Flusberg, 2006). The findings from timbre, amplitude and pitch could also
be compared to the research that focuses on the different brain regions affected by language
37
abilities using MRI scanning (Siegal & Blades, 2003). Previous research highlights the
cerebellum and sub cortical white matter as the key areas that mediate motor control and
ongoing work continues in this area (Bauman & Kemper, 2005; Courchehene, 2002). There
is also a lot research into cry analysis of children with medical and social problems that could
be included in this further research. Studies found that brain damaged children had higher
pitch, more dynamic sound and more unstable fundamental frequency (Corwin, Lester, &
Golub, 1996). The research could focus on whether the children with differences in timbre,
amplitude and pitch findings also display differences in their brain scans. It would also be
relative to compare the timbre, amplitude and pitch findings with those same children later on
in their development to determine who later develops language impairments, such as specific
language impairment, and what their original differences for variables were compared to
normally developing children (Tager-Flusberg, 2006).
It is important to clarify that this study serves only as an indicator for further study
rather than trying to claim substantial findings on its own. The sample size in this study is
extremely small and the results serve only as an insight into early language development
between autistic children and non autistic children. The basis of autism is still very unclear
and there is still a vast amount of research to be conducted. This study simply aims to
highlight an area of interest that may provide further information into autism if more research
is followed up.
This study hopes to provide the foundations to further research in this area. If these
vocal differences are apparent from such an early age, autism could be detected earlier and
therapies are likely to be more effective. It is important to note however, that not all children
who go on to develop autism will display such language differences in early development.
Vocal difficulties in children may only manifest or become an issue much later in
development for many children and it may prove difficult to attempt to detect autism at such
an early stage. It is also important to note that neither verbal difficulties, nor physical
movement explains the whole extent of autism. Autism cannot be diagnosed simply from
these symptoms therefore; children who display similar movement and vocal difficulties may
not be diagnosed with autism.
This study highlights home videos as a reliable method of research. It allows data to
be accessed at any given time and to be considered or compared at a much later date that
38
recorded. The setting is observational and the subjects are in their own environment so the
behaviour is more natural than it would be in a laboratory. As this is the case there are no
variables to control for. There can be problems with home video tapes however. For instance
they can produce very poor sound quality which would be a problem for sound analysis. Not
having the ability to control for certain factors can discourage many researchers from using
this method of research.
Generally, the language of autistic sufferers varies greatly from person to person.
Each case can be taken as an individual one as every child develops differently. It has been
difficult for researchers so to even categorise types of autistics (Tager-Flusberg, 2006). It is
therefore, difficult to generalise the difference in vocalisations from this study to all autistic
sufferers. It is important to note that further studies of this kind may find a varying degree of
difference between autistic and non autistic sufferers in terms of timbre, amplitude and pitch.
This study does not aim to do so and merely serve as an indicator of what kind of controlling
parameters to consider for when analysing a child who possibly has autism.
39
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Appendices
44
Appendix 1
Detailed description of the protocol used in the analysis stages.
Video edit and audio transfer
Review all clips and identify worthy sections.
Take notes on any utterances and/or interactions.
Edit out each clip tagging each file name as ex 1, ex 2, ex 3 (excerpt) using final cut pro.
Export audio section using the same labelling protocol and save as mono.wav file 44100kHz.
Wave processing
Open wav. file using praat (read, read from file)
Save file as a short text file (write, write to short text file (save in corresponding ex folder)).
Right click open with ‘text edit’. Delete first 13 lines (header information). The first and last
lines should be the start and end of the data, nothing else.
Save the changes and close the text file.
TauGUI
Open Matlab, open the TauGUI folder in the workspace and type: TauGUI in the command
window.
TauGUI will then open in a new window.
Go to file open
Select the text file created from the wave file.
Sample rate; 44000Hz
Smoothing filter- 4, run the section seeker
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Enter criteria: max duration- 0.5, min duration- 0.00005
Max amplitude- 1.5, min amplitude- 0.001
Run test
Click select all
Click analyse
When finished it will come up ‘done’ at the bottom of the screen box.
Go to file, export as text file
TKA contour- extraction
Copy and paste the appropriate columns into the wave worker spreadsheet. A, B, C, D, E, F,
and G- and K, L, and N (not including the headers)
Do not include direction or number of gaps or the velocity information.
(make sure there are no blank rows at the bottom of the spreadsheet).
Save excel sheet as ‘name’ waves.excel
DynK- % criteria
(enable macro’s)
from this spreadsheet copy column (L) timesinglemotion- and (I) N_Percentage and (H) R2.
Paste into Kaliedograph.
Smooth the data: Go to Macros, Gauss
Plot a scatterplot to examine % criteria: Go to Gallery, Linear, Plot
Realtime TKA
Copy the following columns from the waves spreadsheet:
BWFcomposite (N), Time (O), Mean UPDOWN by BWF (S), MEAN AMP (T).
Paste (paste special, values, move time column to A column) them into the delete balnk rows
spreadsheet.
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Ensure you only have numeric data (no headers or N/A data, delete whole rows if N/As come
up)
Go to tools- macros, macros, then click select delete blank rows, and run.
Then save data as a TKA file (e.g. test01.tka.xls)
There are now 4 columns of continous numeric information: Time, 1/T (waveduration/
frequency), dynK (k), and AMP (amplitude).
Save TKA file.
Copy and paste these into Kaleidagraph
Smooth the data: Go to Macros, Gauss
Sigma value of 4
(column 1=4, 2=5, 3=6, add in headings: 1/t_g4, k_g4, A_g4)
Save Kaleidagraph file (click on file name and add ‘_g4’ to it and save).
Create plots of 1/T, dynK and A and save these with the data (gallery, linear, scatter). Make
sure appropriate scaling is applied. This is a good chance to compare the plots of non and
smoothed data.
Delete non-smoothed rows and columns. Go to save as format (at the bottom) and tab
delimited to save as a text file.
Open text file in text edit and remove any header information to ensure only numeruc
information remains in the file (top and bottom).
In Matlab, clear the workspace (make sure it is clear!)
Import data (under the workspace)
Open resample.m (in current directory, my folder)
Edit lines 1,2,3,4 so they have the same name as the txt.file you are resampling
(e.g. Line 1 “exact file name except extension”)
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In line 8 (nsteps) you need to enter the number of new samples
Which is a calculation: end time-start time/ 0.002= number of steps
(for 500, 1000 would be= 0.001)
Round the result to the nearest whole number.
Click the save and run script button of the editor window (green play button)
In the workspace you notice a bunch of new files
The data has been resampled but you must now convert these into .txt files that can be
analysed in the TauGUI.
Go to File, close editor.
In the command window execute the following commands replacing the name of your file
where you see name. (e.g., Test01_TKA_g4T=[Dur2]
Now export these individual files (one for T, one for K, and one for A) as .txt files using the
following command line in the Matlab command window (e.g., save test01_TKA_g4T.txt
test_TKA_g4T_ASCII).
Save.
You now have T, K, and A- contours for the appropriate section at the sample rate of 500Hz.
Open each in the TauGUI- select 500Hz. And enter the appropriate values in the min and
max these will be dictated by the data you have- i.e. if a vocalisation is very loud you will
need a larger range in the gap size criteria.
Suggested sigma 4.
Use 5% velocity
And min duration 0.02 (you should not go lower than 0.02 as for the recursive analysis 0.02
will provide 10 data points at 500Hz- if you wish to analyse smaller units then you must
resample at a higher frequency).
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Select the motion gaps you wish to analyse.
Click analyse- the export as text.
It is recommended that motion gaps identified are marked on the kaleidagraph plots produced
earlier to facilitate interpretation.
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