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An Interlocutor-Modulated Attentional LSTM for Differentiating between Subgroups of Autism Spectrum Disorder
Abstract
Recalling and discussing personal emotional experiences is one of the key procedures in assessing complex affect processing of individuals with Autism Spectrum Disorder (ASD). This procedure is a standard subpart of a diagnostic interview to assess ASD - the Autism Diagnostic Observation Schedule (ADOS). Previous work has demonstrated that the behavior features computed from this procedure in ADOS possess discriminative information between the three distinct ASD subgroups: Autistic Disorder (AD), High Functioning Autism (HFA), and Asperger Syndrome (AS). In this work, we propose an interlocutor-modulated attentional long short term memory network (IM-aLSTM) that models the ASD individual's acoustic features with a novel interlocutor-modulated attention mechanism. Our IM-aLSTM achieves ASD subgroup categorization accuracy of 66.5%, which is a 14% absolute improvement over baseline method on the same database. Our analyses further indicate that the attention weights are concentrated more on interaction segments where the ASD individual is being asked to recall and discuss his/her own negative emotional experiences.
Figures
A Schematic ofour Interlocutor-Modulated Attentional LSTM: Our IM-aLSTM introduces an Interlocutor-Modulated Attention Mechanism to emphasize the important turn-feature during dyadic face-to-face interaction in the Emotion part ofthe ADOS.
A Schematic ofour Interlocutor-Modulated Attentional LSTM: Our IM-aLSTM introduces an Interlocutor-Modulated Attention Mechanism to emphasize the important turn-feature during dyadic face-to-face interaction in the Emotion part ofthe ADOS.
Keywords
behavioral signal processing (BSP) | autism spectrum disorder | dyadic interaction | attention mechanism
Authors
Yun-Shao Lin Chi-Chun Lee
Publication Date
2018/09/02
Conference
Interspeech 2018
Interspeech 2018
DOI
10.21437/Interspeech.2018-1288
Publisher
ISCA