Linguistic and Acoustic Features for Automatic Identification of Autism
Spectrum Disorders in Children’s Narrative
Hiroki Tanaka, Sakriani Sakti, Graham Neubig, Tomoki Toda, Satoshi Nakamura
Graduate School of Information Science, Nara Institute of Science and Technology{hiroki-tan, ssakti, neubig, tomoki, s-nakamura}@is.naist.jp

Abstracta necessary component of understanding the nature of ASD, creating systems for automatic ASD
screening, and early intervention methods such associal skills training and applied behaviour analysis (Wallace et al., 1980; Lovaas et al., 1973).
There are a number of studies finding differences between people with ASD and people withtypical development (TD). In terms of deficits insocial communication, there have been reports describing atypical usage of gestures (Ashley andInge-Marie, 2010), frequency of eye-contact andlaughter (Geraldine et al., 1990), prosody (McCann and Peppe, 2003; Rhea et al., 2005), voice
quality (Asgari et al., 2013), delay responses(Heeman et al., 2010), and unexpected words(Rouhizadeh et al., 2013). In this paper, we particularly focus on the cues of ASD that appear inchildren’s language and speech
In the case of language, Newton et al. (2009)
analyze blogs of people with ASD and TD, andfound that people with ASD have larger variationof usage of words describing social processes, although there are no significant differences in otherword categories. In the case of speech, people with
ASD tend to have prosody that differs from thatof their peers (Kanner, 1943), although McCannand Peppe (2003) note that prosody in ASD is anunder-researched area and that where research has
been undertaken, findings often conflict. Since
then, there have been various studies analyzing
and modeling prosody in people with ASD (Daniel
et al., 2012; Kiss et al., 2013; Santen et al., 2013;
Van et al., 2010). For example, Kiss et al. (2012)
find several significant differences in the pitch
characteristics of ASD, and report that automaticclassification utilizing these features achieves accuracy well above chance level. To our knowledge, there is no previous work integrating bothlanguage and speech features to identify differences between people with ASD and TD. However, it has been noted that differences in person-

Autism spectrum disorders are developmental disorders characterised as deficits
in social and communication skills, andthey affect both verbal and non-verbal
communication. Previous works measured
differences in children with and without
autism spectrum disorders in terms of
linguistic and acoustic features, although
they do not mention automatic identification using integration of these features.
In this paper, we perform an exploratory
study of several language and speech features of both single utterances and full narratives. We find that there are characteristic differences between children with
autism spectrum disorders and typical development with respect to word categories,
prosody, and voice quality, and that thesedifferences can be used in automatic classifiers. We also examine the differences
between American and Japanese children
and find significant differences with regards to pauses before new turns and linguistic cues.

1

Introduction

Autism spectrum disorders (ASD) are developmental disorders, first described by Kanner andAsperger in 1943 and 1944 respectively (Kanner,
1943; Asperger, 1944). The American Psychiatric Association defines the two characteristics of
ASD as: 1) persistent deficits in social communication and social interaction across multiple contexts, and 2) restricted, repetitive patterns of behavior, interests, or activities (American Psychiatric Association, 2013). In particular, the former
deficits in social communication are viewed as themost central characteristic of ASD. Thus, quantifying the degree of social communication skills is

88
Workshop on Computational Linguistics and Clinical Psychology: From Linguistic Signal to Clinical Reality, pages 88–96,
Baltimore, Maryland USA, June 27, 2014. c 2014 Association for Computational Linguistics

ality traits including introversion/extroversion canbe identified using these features (Mairesse et al.,2007).
In this paper, we perform a comprehensive analysis of language and speech features mentioned inprevious works, as well as novel features specificto this work. In addition, while previous works analyzed differences between people with ASD andTD, we additionally investigate whether it is possible to automatically distinguish between children
with ASD or TD using both language and speechfeatures and a number of classification methods.
We focus on narratives, where the children serving
as our subjects tell a memorable story to their parent (Davis et al., 2004). Here, the use of narrative
allows us to consider not only single-sentence features, but also features considering interaction aspects between the child and parent such as pauses
before new turns and overall narrative-specific features such as words per minute and usage of unexpected words. Given this setting, we performa pilot study examining differences between children with ASD and TD, the possibilities of automatic classification between ASD and TD, and thedifferences between American and Japanese children.

2

and video of the child and the parent.
In this paper, we use narrative data of four children with ASD (male: 3, female: 1) and twochildren with TD (male: 1, female: 1) as an exploratory study. The intelligence quotient (IQ) forall subjects is above 70, which is often used asa threshold for diagnosis of intellectual disability. Each subject’s age and diagnosis as ASD/TD
is provided in Table 1. In the narrative session,
each child and parent speaks “a memorable story”
for 5 minutes in turn, and the listener responds tothe speaker’s story by asking questions. After 5
minutes, the experimenter provides directions tochange the turn.
Table 1: Subjects’ age and diagnosis
Subject
Age
Diagnosis

A1
10
ASD

A2
10
ASD

A3
10
ASD

A4
13
ASD

T1
10
TD

T2
12
TD

In this paper, we analyze the child-speaking turnof the narrative session in which the parent responds to the child’s utterances. All utterances aretranscribed based on USC Rachel corpus manual(Mower et al., 2011) to facilitate comparison withthis existing corpus. In the transcription manual, if
the speaker pauses for more than one second, thespeech is transcribed as separate utterances. In thispaper, we examine two segment levels, the firsttreating each speech segment independently, andthe second handling a whole narrative as the target. When handling each segment independently,
we use a total of 116 utterances for both children
with ASD and TD.

Data Description

As a target for our analysis, we first collected adata set of interactions between Japanese children
and their parents. In collecting the data, we followed the procedure used in the creation of the
USC Rachel corpus (Mower et al., 2011). The dataconsists of four sessions: doh (free play), jenga (agame), narrative, and natural conversation. The
first child-parent interaction is free play with theparent. The child and parent are given play doh,
Mr. Potato Head, and blocks. The second childparent interaction is a jenga game. Jenga is a gamein which the participants must remove blocks, one
at a time, from a tower. The game ends when thetower falls. The third child-parent interaction is anarrative task. The child and parent are asked to
explain stories in which they experienced a memorable emotion. The final child-parent interaction
is a natural conversation without a task. These
child-parent interactions are recorded and will enable comparison of the child’s interaction style andcommunication with their parent. Each session
continues for 10 minutes. During interaction, a pin
microphone and video camera record the speech

3 Single Utterance Level
In this section, we describe language and speechfeatures and analysis of these characteristics towards automatic classification of utterances basedon whether they were spoken by children with
ASD or TD. We hypothesize that based on the features extracted from the speech signal we are capable to classify children with ASD and TD on a
speech segment level, as well as on narrative level
after temporally combining all the segment-based
decisions.
3.1 Feature Extraction
We extract language and speech features basedon those proposed by (Mairesse et al., 2007) and89

(Hanson, 1995). Extracted features are summarized in Table 2. We also add one feature not covered in previous work counting the number of occurrences of laughter.

3.1.2 Speech Features
For speech feature extraction, we use the Snacksound toolkit3 . Here, we consider fundamental
frequency, power, and voice quality, which are effective features according to previous works (McCann and Peppe, 2003; Hanson, 1995). We do
not extract mean values of fundamental frequency
and power because those features are strongly related to individuality. Thus, we extract statistics
of standard deviation (fsd, psd) and coefficient ofvariation (fcov, pcov) for fundamental frequency
and power. We calculate speech rate, which is afeature dividing the number of words by the number of voiced seconds. Voice quality is also computed using: the amplitude of the third formant(a3), the difference between the first harmonic andthe second harmonic (h1h2), and the difference
between the first harmonic and the third formant(h1a3) (Hanson, 1995).

Table 2: Description of language and speech features.
Language
General descriptor
Sentence structure
Psychological proc.
Personal concerns
Paralinguistic
SpeechPitch
Intensity
Speech rate
Voice quality

FeaturesWords per sentence (WPS)
Words with more than 6 letters
Occurrences of laughter
Percentage of pronouns, conjunctions,
negations, quantifiers, numbers
Percentage of words describing social,affect, cognitive, perceptual,and biological
Percentage of words describing work,
achievement, leisure, and home
Percentage of assent,
disfluencies, and fillers
Features
Statistics of sd and cov
Statistics of sd and cov
Words per voiced second
Amplitude of a3
Difference of the h1 and the h2
Difference of the h1 and the a3

3.1.3 Projection Normalization
For normalization, we simply project all feature
values to a range of [0, 1], where 0 corresponds
to the smallest observed value and 1 to the largest
observed value across all utterances. For utterancei, we define the value of the jth feature as vij andvij −minj
define pij = max, where pij is the featurej −minj
value after normalisation.

3.1.1 Language Features

3.2 Characteristics of Language and Speech
Features

We use the linguistic inquiry and word count(LIWC) (Pennebaker et al., 2007), which is a tool
to categorize words, to extract language features.
Because a Japanese version of LIWC is not available and there is no existing similar resource forJapanese, we implement the following procedures
to automatically establish correspondences between LIWC categories and transcribed Japaneseutterances. First, we use Mecab1 for part-ofspeech tagging in Japanese utterances, translateeach word into English using the WWWJDIC2
dictionary, and finally determine the LIWC category corresponding to the English word. Amongthe language features described in Table 2, we
calculate sentence structures, psychological processes, and personal concerns using LIWC, andother features using Mecab. Here, we do notconsider language-dependent features and subcategories of LIWC.
1
2

In this section, we report the result of a t-test, principal component analysis, factor analysis, and decision tree using the normalised features. We use
R4 for statistical analysis.
Table 3 shows whether utterances of children
with ASD or TD have a greater mean on the corresponding feature. The results indicate that thechildren with ASD more frequently use words
with more than 6 letters (e.g. complicated words),
assent (e.g. “uh-huh,” or “un” in Japanese), andfillers (e.g. “umm,” or “eh” in Japanese) significantly more than the children with TD. In contrast, the children with TD more frequently use thewords words categorized as social (e.g. friend), affect (e.g. enjoy), and cognitive (e.g. understand)significantly more than the children with ASD. In
addition, there are differences in terms of fundamental frequency variations and voice quality (e.g.
3https://code.google.com/p/mecab/
http://www.edrdg.org/cgi-bin/wwwjdic/wwwjdic?1C490

http://www.speech.kth.se/snack/http://www.r-project.org

Table 3: Difference of mean values between ASD and TD based on language and speech features fromchildren’s utterances. Each table cell notes which of the two classes has the greater mean on the corresponding feature (*: p < 0.01, **: p < 0.005).
WPS
affectTD**
nonfluent-

6 let.
ASD*
cognitiveTD*
fillers
ASD*

laughterperceptual
fsd
TD**

adverbbiological
fcov
TD*

pronounrelativity
psd-

conjunctions
work
pcov
-

h1a3). In particular, we observe that the children
with ASD tend to use monotonous intonation asreported in (Kanner, 1943). We do not confirm asignificant differences in other features.
Next, we use principal component analysis andfactor analysis to find features that have a large
contribution based on large variance values. As
a result of principal component analysis, features
about fundamental frequency, power, and h1a3
have large variance in the first component, and thefeature counting perceptual words also has large
value in the second component. To analyze a different aspect of principal component analysis withrotated axes, we use factor analysis with the varimax rotation method. Figure 1 shows the result offactor analysis indicating that features regarding
fundamental frequency and power have large variance. In addition, other features such as speechrate, a3, and h1a3 also have large variance. Here,
we can see that for features such as statistics offundamental frequency (fsd and fcov) and power(psd and pcov), the correlation coefficient between
these features are over 80% (p < 0.01). For correlated features, we use only standard deviation in
the following sections.
We also analyze important features to distinguish between children with ASD and TD by using a decision tree. Figure 2 shows the result of adecision tree with 10 leaves indicating that speechfeatures fill almost all of the leaves (e.g. fsd is amost useful feature to distinguish between ASD
and TD). In terms of the language features, we
confirm that WPS and perceptual words are important for classification.

negationsachievementspeech rate-quantifiers
leisurea3-

numbers
home
h1h2
-

socialTD**assent
ASD**
h1a3
ASD**

Figure 1: Factor analysis with varimax rotation
method. First and second factors are indicated.
feature sets: 1) language features (Language), 2)
speech features (Speech), 3) all features (All), 4)
important features according to the t-test, principal component analysis, factor analysis, and decision tree (Selected), 5) important features according to the t-test that are not highly correlated(T-Uncor). The feature set of T-Uncor is as follows: 6 let., social, affect, cognitive, fillers, assent, fed, and h1a3. We also show the chance
rate, which is a baseline of 50% because the number of utterances in each group is the same, andmeasure accuracy with 10-fold cross-validationand leave-one-speaker-out cross-validation using
naive Bayes (NB) and support vector machineswith a linear kernel (SVM). In the case of leaveone-speaker-out cross-validation, we use T-Uncor
because the number of utterances without one
speaker is too small to train using high dimensional feature sets.

3.3 Classification
In this section, we examine the possibility of automatic identification of whether an utterance belongs to a speaker with ASD or TD. Based on
the previous analysis, we prepare the following

Table 4 shows the result indicating that accu-

91

4

0.5
0.1

0.2

0.3

0.4

TD
ASD

0.0

Table 4: Accuracy using Naive Bayes and SVM
classifiers. The p-value of the t-test is measured
compared to baseline (chance rate) (†: p < 0.1, *:p < 0.01)
Feature set
Accuracy [%]Baseline
NB
SVM
Language62.2† 70.3*
Speech
57.6 67.6*
All
50.0
65.0† 68.8*
Selected
67.4* 71.9*
T-Uncor67.8† 68.1†
Per-Speaker
50.0
65.5† 66.7†

Exponential/Gamma probability values

racies with almost all feature sets and classifiers
are over 65%. The SVM with Selected achieves
the best performance for the task of 10-fold crossvalidation, and The SVM with T-Uncor achieves
66.7% for the task of leave-one-speaker-out. The
accuracy for the task of leave-one-speaker-out oneach speaker A1 to T2 is as follows: 78%, 60%,
53%, 51%, 82%, and 78%.

2

4

6

8

10

Pauses before new turns (sec)Figure 3: Gamma/Exponential pause distributions
with parameters computed using Maximum Likelihood Estimation (MLE) for children with ASD
and TD.
ter fitted distribution. All subjects significantly fit(p > 0.6). As shown in Figure 3, we confirm thatchildren with ASD tend to delay responses to theirparent compared with children with TD. To reflect
this information in our following experiments in
automatic identification of ASD in narrative, we
extract the expectation value of the exponential
distribution

Narrative Level

In this section, we focus on the features of entire narratives, which allows us to examine otherfeatures of child-parent interaction for a better understanding of ASD and classification in children
with ASD and TD. Each following subsection describes the procedure of feature extraction andanalysis of characteristics at the narrative level.
We consider pauses before new turns and unexpected words, which are mentioned in previous
works, as well as words per minute.

Heeman et al., (2010) also reported the relationship of the parent’s previous utterance’s type(question or non-question) and the child’s pauses.
We examine the relationship between the parent’s
previous question’s type and pauses before newturns. For each of the children’s utterances, welabel the parent’s utterance that directly precedes
as either “open question,” “closed question,” or“non-question”, and we calculate pause latency.
Closed-questions are those which can be answeredby a simple “yes” or “no,” while open-questions
are those which require more thought and morethan a simple one-word answer. As shown in Table
5, children with ASD tend to delay responses totheir parent to a greater extent than children with
TD. We found no difference between open andclosed questions, although a difference between
questions and non-questions is observed. These
results are consistent with those of previous work
(Heeman et al., 2010) in terms of differences between questions and non-questions.

4.1 Pauses Before New Turns
Heeman et al., (2010) reported that children with
ASD tend to delay responses to their parent morethan children with TD in natural conversation. In
this paper, we examine whether a similar result isfound in interactive narrative. We denote valuesof pauses before new turns as time between theend of the parent’s utterance and the start of thechild’s utterance. We do not consider overlap ofutterances. We test goodness of fit of pauses to a
gamma and an exponential distribution based on(Theodora et al., 2013), because the later is a special case of gamma with a unity shape parameter,using the Kolmogorov-Smirnov test.
Figure 3 shows a fitting of pauses to gamma
or exponential distributions, and we select a bet-

92

fsd < 0.366375|

psd < 0.306304

fcov < 0.308899

WPS < 0.0543478

fcov < 0.204553

aa

pcov < 0.46429

afsd < 0.513756

t

t

t

a

perceptual < 0.07

pcov < 0.230634

a

a

t

Figure 2: Decision tree with 10 leaves (a: ASD, t: TD).
Table 5: Relationship of pauses before new turns
and parents’ question types. The mean value andstandard deviation are shown.
Question type
Closed-question
Open-question
Non-questionTD
0.47 (0.46)
0.43 (0.34)
0.95 (1.18)

Table 6: Mean value of words per minute.
Subj.
A1
A2
A3
A4
T1
T2

ASD
1.61 (1.87)
1.76 (1.51)
2.60 (3.64)

Averaged WPM
18.25
86.75
23.75
115.5
99.25
103.5

4.2 Words Per Minutepriate usage of words (Rouhizadeh et al., 2013).
We evaluate these unexpected words using two
measures, term frequency-inverse document frequency (TF-IDF) and log odds ratio. We use
the following formulation to calculate TF-IDF foreach child’s narrative i and each word in that narrative j, where cij is the count of word j in narrative i. fj is the number of narratives from the full
data of child narratives containing that word j, andD is the total number of narratives (Rouhizadeh etal., 2013).

We analyze words per minute (WPM) in children
with ASD and TD to clarify the relationship between ASD and frequency of speech. We use a
total of 5 minutes of data in each narrative, andthus the total number of words are divided by 5 to
calculate WPM. Table 6 shows the result. The datain this table indicates that some children with ASD
have a significantly lower speaking rate than others with TD, but it is not necessarily the case that
ASD will result in a low speaking rate such as thecase of Asperger’s syndrome (Asperger, 1944).
4.3 Unexpected Words

tf − idfij = (1 + log cij ) log

Characteristics of ASD include deficits in social
communication, and these deficits affect inappro-D
fj

The log odds ratio, another measure used in in-93formation retrieval and extraction tasks, is the ratio
between the odds of a particular word, j, appearing in a child’s narrative, i. Letting the probability of a word appearing in a narrative be p1 andthe probability of that word appearing in all othernarratives be p2 , we can express the odds ratio as
follows:
odds ratio =with a K-NN classifier with K=1 nearest neighbour. As features, we compute the features mentioned in Section 3.1, and use the average over allutterances as the features for the entire narrative.
Finally, we use pauses before new turns (expectation value of the exponential distribution), WPM,
TF-IDF, log odds ratio, 6 let., social, affect, cognitive, assent, fillers, fsd, h1a3, and calculate accuracy with leave-one-speaker-out cross-validation.
As a result, we achieved an accuracy of 100%in classification between ASD and TD on the fullnarrative level, which shows that these features
are effective to some extent to distinguish children
with ASD and TD. However, with only a total of 6
children, our sample size is somewhat small, andthus experiments with a larger data set will be necessary to draw more firm conclusions.

odds(p1 )p1 /(1 − p1 )=odds(p2 )p2 /(1 − p2 )A large TF-IDF and log odds score indicatesthat the word j is very specific to the narrativei, which in turn suggests that the word might be
unexpected or inappropriate. In addition, because
the overall amount of data included in the narratives is too small to robustly analyze these statistics for all words, we also check for the presenceof each word in Japanese WordNet5 and determine that if it exists in WordNet it is likely a common (expected) word. Table 7 shows the resultof TF-IDF, log odds ratio, and their summation,
and we confirm that there is no difference between
children with ASD and TD. This result is different from that of previous work (Rouhizadeh et al.,2013). The children in that study were all tellingthe same story, and one possible explanation forthis is due to the fact that in this work we do
not use language-constricted data such as narrative
retelling, and thus differences due to individuality
are more prevalent.

5 Data Comparison
As all our preceding experiments have been performed on data for Japanese child-parent pairs, it
is also of interest to compare these results withdata of children and parents from other cultures.
In particular, we refer to the USC Rachel corpus(Mower et al., 2011) (the subjects are nine children with ASD) for comparison. Using the USC
Rachel corpus, there is a report mentioning the relationship of parent’s and child’s linguistic information and pauses before new turns (Theodora etal., 2013). In this paper, we follow this work using Japanese data. The USC Rachel corpus includes a session of child-parent interaction, andthe same transcription standard is used. We extract pauses before new turns, and short and longpauses are differentiated based on the 70th percentile of latency values for each child individually. We investigate the relationship between theparent and child’s language information based onfeatures used in Section 3.1, and short and longpauses.
Table 8 and 9 show significantly greater meanvalues performed using bootstrap significancetesting on the means of the two pause types. By
observing the values in the table, we can seethat the trends are similar for both American andJapanese children. However, in terms of WPS,
there is a difference. The American ASD children have greater means for WPS in the case oflong pauses, while Japanese children have greatermeans for WPS in the case of short pauses. We
analyze these differences in detail.

Table 7: TF-IDF, log odds ratio, and their summation.
Subj. TF-IDF Log-odds T+L
A1
0.50
1.01
1.52
A2
0.58
0.49
1.08
A3
0.66
1.23
1.89
A4
0.66
0.31
0.96
T1
0.74
0.49
1.23
T2
0.62
0.44
1.06

4.4 Classification
In this section, we examine the possibility of automatic classification of whether an interactive narrative belongs to children with ASD or TD. Because of the total number of subjects is small (n=4
for ASD, n=2 for TD), we perform classification
5

http://www.omomimi.com/wnjpn/

94

nonflu.
L*
S†
L*
S†

adverb
L*
L*
L*
L*
L*
L*
L†
L*
L*

Parentcogn. percept.
L*
L*
L*
L*
L*
L*
L*
L*
L*
L*
L*
L*
L*
L*
L*

Table 9: Bootstrap for pause differences in theJapanese corpus.
Subj.
A1
A2
A3
A4

WPS
S*
S†
S†
S*

Child
conj. affectS*-nonflu.
-adverbS*
L*
L*-

Parentcogn. percept.
L*
L*
L*
L*
L*
-

In the Japanese corpus, we observe that WPS islarger in the case of short pauses. As we noticed
that the child often utters only a single word for
responses that follow a long pause, we analyzed
the content of these single word utterances. As
shown in Figure 4, for example, A1 tends to use
a word related to assent when latency is long, andA4 tends to use a word related to filler, assent orothers when latency is long. Though there are individual differences, we confirm that the Japanesechildren with ASD examined in this study tend
to delay their responses before uttering one word.
These characteristics may be related to the parent’s
question types and the child’s cognitive process,and thus we need to examine these possibilities indetail.

6

0.8
0.6
0.4
0.2

Child
conj. affect
L*
S*
L*
S†
L†
S*
S†
-

Percentage of one−word responces

S1
S2
S3
S4
S5
S6
S7
S8
S9

WPS
L*
L*
L*
L†
L*
L†
L*
-

OthersLaughFillerAssent

0.0

Subj.

1.0

Table 8: In the case of USC Rachel corpus, bootstrap on difference of means between short (S) andlong (L) pauses based on linguistic features fromchild’s and parent’s utterances (†: p < 0.1, *: p <0.01). Each table cell notes which of the two typesof pauses has greater mean on the corresponding
feature.

A1

A2

A3

A4

Subject

Figure 4: The language category of one-word responses in the case of a long pause.
level and the narrative level. We examined features mentioned in a number of previous works, aswell as a few novel features. We confirmed about
70% accuracy in an evaluation over single utterances, and some narrative features also proved tohave a correlation with ASD.
For future directions, we plan to perform larger
scale experiments to examine the potential of thesefeatures for automated ASD screening. Given the
results of this, we plan to move to applications including the development of dialogue systems forautomatic ASD screening and social skills training.

Acknowledgments
We would like to thank the participants, children
and their parents, in this study. We also thankDr. Hidemi Iwasaka for his advice and support asclinician in pediatrics. A part of this study was
conducted in Signal Analysis and Interpretation
Laboratory (SAIL), University of Southern California. This study is supported by JSPS KAKEN
24240032.

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