Classification of Children with Autism Spectrum and Typically Developing Children Using Pitch Features

Transcription

1 THE INSTITUTE OF ELECTRONICS, INFORMATION AND COMMUNICATION ENGINEERS TECHNICAL REPORT OF IEICE kakyhara@me.cs.scitec.kobe-u.ac.jp, {takigu,ariki,satoshi}@kobe-u.ac.jp, nakai@jc.kawasaki-m.ac.jp SVM % SVM Classification of Children with Autism Spectrum and Typically Developing Children Using Pitch Features Yasuhiro KAKIHARA, Tetsuya TAKIGUCHI, Yasuo ARIKI, Yasushi NAKAI, and Satoshi TAKADA Graduate School of System Informatics, Kobe University 1 1 Rokkodai-cho, Nada-ku, Kobe, Hyogo, Japan Research Center for Urban Safety and Security, Organization of Advanced Science and Technology, Kobe University 1 1 Rokkodai-cho, Nada-ku, Kobe, Hyogo, Japan Department of Nursing Childcare, Kawasaki College of Allied Health Professions 316 Matsushima, Kurashiki, Okayama Japan Graduate School of Health Sciences, Kobe University Tomogaoka, Suma-ku, Kobe, Hyogo, Japan kakyhara@me.cs.scitec.kobe-u.ac.jp, {takigu,ariki,satoshi}@kobe-u.ac.jp, nakai@jc.kawasaki-m.ac.jp Abstract Recent investigations have demonstrated that the early support which specialized in autistic spectrum obstacle, such as Picture Exchange Communication System (PECS) Applied Behavier Analysis (ABA) Social Skills Training (SST), is effective. This paper reports the result of a classification experiment carried out using pitch features for children with autism spectrum. Pitch features consist of 24 dimensions, such as 25th, 50th, 75th percentiles, percentile difference, percentile difference, mean, standard deviation, kurtosis, skewness, maximum, minimum, and range. Key words Autism spectrum disorder, Pitch, Intonation, Acoustic analysis SVM This article is a technical report without peer review, and its polished and/or extended version may be published elsewhere. 1 Copyright 2013 by IEICE

2 1. (PECS) [1] (ABA) [2] (SST) [3] [4] [5] [6], [7] [8], [9], [10], [11], [12] SVM (i) (ii) (iii) (i) STRAIGHT [13] (ii) (iii) SVM Percentiles Statistical moment Extreme 3. 1 Table 1 Statistic. 25th Percentile, Δ25th Percentile 50th Percentile, Δ50th Percentile 75th Percentile, Δ70th Percentile Percentile Difference, Δ25-50 Percentile Diff Percentile Difference, Δ50-75 Percentile Diff. Mean, ΔMean Standard Deviation, ΔStandard Deviation Kurtosis, ΔKurtosis Skewness, ΔSkewness Maximum, ΔMaximum Minimum, ΔMinimum Range (Max.-Min.), ΔRange SVM (50 ) [14] STRAIGHT 1,143 1, DSM-IV-TR Table 2 2 Children with Autism spectrum & typically developing children. Children with Autism Spectrum Typically Developing Children Num. of children Age Average age 7.25± ±1.75 Male:Female 24:6 23:31 4. (A) (B) (C) (A) (B)

3 1 Fig. 1 Pitch feature extraction. (C) SVM Leave one out [%] [%] [%] 3 3 Table 3 Classification result for each word. Word ASD TD Sum Acc. [%] Presicion [%] Recall [%] /ashi/ /enpitsu/ /inu/ /kirin/ /megane/ /ringo/ /sakana/ /semi/ /terebi/ /tsukue/ Fig. 3 Classification result for each word % 70% SVM

4 Fig. 2 2 Distribution map of children with Autism spectrum and typically developing children

5 fold 4 Fig. 4 Example of pitch interval divided into three sections Case 1st Interval 2nd Interval 3rd Interval Dim. Case Dim=48 4 Table 4 Classification result for three divided sections. 1st Interval 2nd Interval 3rd Interval Dim. Acc. [%] Case Case Case Case Case Case Case Case1 Case7 Case1 74.9% 2 48 Case2 4 Case2 Case Case5 7 Case5 Case2 4 Case6 Case7 70% SVM Case Percentiles Statistical moment Extreme Dim. 5 Table 5 Classification result for each feature. Percentiles Statistical moment Extreme Dim. Acc. [%] Case Case Case Case Case Case Case Case8 Case

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