Special Children Research Papers - Academia.edu (original) (raw)
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Recognition of emotion-based utterances from speech has been produced in a number of languages and utilized in various applications. This paper makes use of the spoken utterances corpus recorded in Urdu with different emotions of normal... more
Recognition of emotion-based utterances from speech
has been produced in a number of languages and utilized in
various applications. This paper makes use of the spoken
utterances corpus recorded in Urdu with different emotions of
normal and special children. In this paper, the performance of
learning classifiers is evaluated with prosodic and spectral
features. At the same time, their combinations considering
children with autism spectrum disorder (ASD) as noise in terms
of classification accuracy has also been discussed. The
experimental results reveal that the prosodic features show
significant classification accuracy in comparison with the spectral
features for ASD children with different classifiers, whereas
combinations of prosodic features show substantial accuracy for
ASD children with J48 and rotation forest classifiers. Pitch and
formant express considerable classification accuracy with MFCC
and LPCC for special (ASD) children with different classifiers.